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In this task, you are given a question and answer options for that question. Using this information, you have to classify each text into different topics: medicine, nursery, psychology, chemistry, pharmacology, biology.
Input: Consider Input: Question: When administering oxygen therapy to a patient admitted to the emergency room due to exacerbated COPD, you should take into account that:
Options: <0> The flow of oxygen must be high, since respiratory failure is accompanied by hypoxia. <1> The concentration of oxygen is conditioned by the patient's clinical situation. <2> The concentration of oxygen must be equal to the atmospheric concentration, since the patient presents hypercapnia. <3> The flow should be low, since hypoxia is the only stimulus to breathe. <4> The flow must be adjusted to achieve a PO2 greater than 90 mmHg.
Output: nursery
Input: Consider Input: Question: After the intake increases the release of:
Options: <0> Insulin. <1> ACTH. <2> Cortisol <3> Adrenaline from the adrenal medulla. <4> GH
Output: biology
Input: Consider Input: Question: In the urea cycle:
Options: <0> Enzymes that participate are located in the mitochondria. <1> Genetic defects can be treated with benzoate. <2> An intermediate metabolite is N-acetylglutamate. <3> Lysine is synthesized. <4> Carbamylphosphate synthetase II intervenes.
|
Output: chemistry
| 2
|
NIv2
|
task1434_head_qa_classification
|
fs_opt
|
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] |
Given the task definition, example input & output, solve the new input case.
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
Example: [-83, 53, -48, 8]
Output: [-80, 50, -50, 10]
The output correctly rounds each integer in the input list to the nearest ten. So this is a good example.
New input case for you: [510, 136, 287, -719, -249, -533, 476]
Output:
|
[510, 140, 290, -720, -250, -530, 480]
| 1
|
NIv2
|
task373_synthetic_round_tens_place
|
fs_opt
|
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] |
Generate an overlapping word between the given two sentences. When you find the overlapping words, they don't have to match exactly, e.g., "survival" and "survive" are valid overlapping words. Little words like "the" or "of" don't count! You must generate significant words which are not the stop words.
--------
Question: Sentence1: An electric current is a flow of electric charge.
Sentence2: a magnet moving in a coil of copper wire can cause a flow of electric charge.
Answer: charge
Question: Sentence1: Here, the lungs supply the blood with oxygen.
Sentence2: Brain cells die quickly if their blood supply is cut off.
Answer: blood
Question: Sentence1: braking can cause skidding.
Sentence2: Stopping or slowing for turns can cause skidding.
Answer:
|
cause
| 7
|
NIv2
|
task039_qasc_find_overlapping_words
|
fs_opt
|
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] |
Instructions: Given a trivia question, classify broad topical category from this list: 'theater', 'geology', 'book', 'tv', 'astronomy', 'aviation', 'military', 'government', 'boxing', 'projects', 'metropolitan_transit', 'law', 'venture_capital', 'broadcast', 'biology', 'people', 'influence', 'baseball', 'spaceflight', 'media_common', 'cvg', 'opera', 'olympics', 'chemistry', 'visual_art', 'conferences', 'sports', 'language', 'travel', 'location', 'award', 'dining', 'martial_arts', 'comic_strips', 'computer', 'user', 'tennis', 'music', 'organization', 'food', 'event', 'transportation', 'fictional_universe', 'measurement_unit', 'meteorology', 'distilled_spirits', 'symbols', 'architecture', 'freebase', 'internet', 'fashion', 'boats', 'cricket', 'film', 'medicine', 'finance', 'comic_books', 'celebrities', 'soccer', 'games', 'time', 'geography', 'interests', 'common', 'base', 'business', 'periodicals', 'royalty', 'education', 'type', 'religion', 'automotive', 'exhibitions'.
Input: As President of Congress, whose signature dominates the Declaration of Independence?
Output:
|
user
| 3
|
NIv2
|
task900_freebase_qa_category_classification
|
zs_opt
|
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] |
In this task, you're given reviews from Amazon's food products and a summary of that review. Your task is to classify whether the given summary matches the original review. Generate "True" if the given review and its summary match, otherwise generate "False".
Reviews: I bought it for my Android phone, and it never connected. It connected with my laptop just fine though, but that's hardly useful. Several months down the line, I tried using it again, and it still did not connect. I could not find the paper manual, so I tried looking up the manual online, and ... nothing! No warranty either.
Summary: Failed to connect to device -- no user guide online
|
True
| 0
|
NIv2
|
task1309_amazonreview_summary_classification
|
zs_opt
|
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] |
Teacher: Given a phrase describing the relationship between two words, extract the words and the lexical relationship between them. The relation has to be of the type 'MemberOf', 'MadeOf', 'Synonym', 'Entails', 'HasA', 'HasProperty', 'PartOf', 'Antonym' or 'IsA'. The output should have the format: word1 relation word2.
Teacher: Now, understand the problem? If you are still confused, see the following example:
account can be used with the same meaning of report
Solution: account Synonym report
Reason: The output is correct as the input describes the relationship between account and report, and the relation is that of having the same meaning, therefore they have a Synonym relationship.
Now, solve this instance: aid can be used with the same meaning of help
Student:
|
aid Synonym help
| 2
|
NIv2
|
task1510_evalution_relation_extraction
|
fs_opt
|
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] |
Teacher:You are given a sentence in Persian. Your job is to translate the Farsi sentence into Portugese.
Teacher: Now, understand the problem? Solve this instance: مثل یک کودک نوپای مست می مانید.
Student:
|
É como se fôssemos um bebé bêbedo.
| 6
|
NIv2
|
task1273_ted_translation_fa_pt
|
zs_opt
|
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] |
The input is a sentence. The sentence includes an emotion. The goal of the task is to classify the emotion in the sentence to one of the classes: 'fear', 'joy', 'anger', 'sadness'. The emotion mainly depends on the adverb within the sentence.
Q: My mother found herself in an annoying situation.
A: anger
****
Q: Roger feels discouraged.
A: fear
****
Q: I made Kristin feel furious.
A:
|
anger
****
| 4
|
NIv2
|
task1338_peixian_equity_evaluation_corpus_sentiment_classifier
|
fs_opt
|
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] |
Teacher:In this task you will be given a list of numbers and you need to find the mean (average) of that list. The mean of a list can be found by summing every number in the list then dividing the result by the size of that list. The output should be rounded to 3 decimal places.
Teacher: Now, understand the problem? Solve this instance: [-27.297, -51.581, 46.026, -25.783, -26.542, 22.99, 228.991]
Student:
|
23.829
| 6
|
NIv2
|
task094_conala_calculate_mean
|
zs_opt
|
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In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to create the question. Note that the answer to the question should be exactly the given answer, and if the answer is none, the answer to the question shouldn't be obtainable from the passage or linked information.
Passage: MacDonald was involved in raising funds for the Nationalist Party from 1925 and by 1930 was its general secretary in Western Australia. He was elected to the Senate at the 1934 election, representing the United Australia Party, although he took his seat early in March 1935, filling a casual vacancy. He was minister without portfolio assisting the Minister for Commerce from November 1937 to November 1938 and then assisting the Treasurer until April 1939. Due to his support for Billy Hughes for the leadership after the death of Joseph Lyons, MacDonald was left out of Robert Menzies' ministries. He lost his seat at the September 1946 election, with his term finishing in June 1947. He failed to get Liberal Party endorsement for the December 1949 election.
Link Information: none Answer: none
How many members did the United Australia Party have when MacDonald was elected to the Senate to represent them?
Passage: Culberson was signed by the Dodgers in November 2015 and invited to spring training, where he impressed new Dodgers manager Dave Roberts. After an injury to Howie Kendrick opened a spot for him, Culberson made the Dodgers 2016 Opening Day roster. He was designated for assignment in July and optioned to the Oklahoma City Dodgers, then recalled a month later. Culberson ended the big club's final home game of the season with a walk-off home run against former Rockies teammate Boone Logan, clinching the Dodgers' fourth consecutive National League West division title. He played in 34 games for Los Angeles, hitting .284 and in 70 games for Oklahoma City, hitting .260. He had no hits in seven at-bats with two strikeouts in the 2016 National League Division Series. He was outrighted to the minors and removed from the 40-man roster on December 9, 2016. He was assigned to Oklahoma City to begin the season. He remained in the minors for the conclusion of the AAA season, hitting .250 in 108 games and returned to the majors with the Dodgers on September 4. In 15 games for the Dodgers, he hit .154 with two hits in 13 at-bats. When starting shortstop Corey Seager was left off of the 2017 NLCS roster because of a back injury, Culberson was added to the post-season roster. He appeared in all five games of the series, with five hits in 11 at-bats for a .455 average. In the 2017 World Series, Culberson had three hits in five at-bats, including his first career post-season home run, which he hit off Chris Devenski of the Houston Astros in the 11th inning of Game Two.
Link Information: Culberson was signed by the Dodgers in November 2015 Answer: 43
How old was Dave Roberts the year Culberson was signed to the Dodgers?
Passage: In July 1994, two students of Taipei First Girls' High School committed suicide by charcoal-burning in a hotel in Su'ao, Yilan with a note that didn't state the reason clearly, even though that it was suspected in some mass media that they were a lesbian couple. In November 1998, a middle-aged woman in Hong Kong died by suicide using this method inside her small, sealed bedroom. She had a chemical engineering background. Hong Kong was suffering from an economic depression at the time, and suicide in general was increasing. After the details of this suicide were highly publicised by local mass media, many others killed themselves in this way (an example of the Werther effect). Within two months, charcoal-burning had become the third major suicide killer in Hong Kong. Charcoal-burning suicide accounted for 1.7% of Hong Kong suicides in 1998 and 10.1% in 1999. By 2001, it had surpassed hanging as the second most-common method of suicide in Hong Kong (second only to jumping), accounting for about 25% of all suicide deaths. The method has since spread to mainland China, Taiwan and Japan.
Link Information: Lin Qinghui (18-year-old, Taipei County, Xinzhuang City) and Shi Jiya (17-year-old, Taipei City) Answer: 18-year-old
|
How old were the two students who committed suicide in July of 1994?
| 0
|
NIv2
|
task236_iirc_question_from_passage_answer_generation
|
fs_opt
|
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] |
Two analogies that signify affordances are given in the form "A : B. C : ?". Affordance is the possibility of an action being done on an object, for example book is an affordance of writing. The phrase "A : B" implies that B is an affordance of A. Your task is to replace the question mark (?) with the appropriate affordance of the given action C, following the "A : B" relation. Your answer should be a single object without further explanation.
stalk : prey. make : ?
promise
hire : employee. wash : ?
dishes
climb : tree. fire : ?
|
employee
| 0
|
NIv2
|
task1153_bard_analogical_reasoning_affordance
|
fs_opt
|
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] |
Q: You are given a question or fill-in-the-blank question, two answer options (Option1 and Option2) and an Explanation. Your task is to find the correct answer (return the string of the correct option, not option1/2) for the given question from the given options and using explanation.
Question: The electrons with the _____ energy are farthest to the nucleus.
Option1: most
Option2: least
Explanation: Electrons at higher energy levels, which are farther from the nucleus, have more energy.
A:
|
most
| 7
|
NIv2
|
task178_quartz_question_answering
|
zs_opt
|
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] |
Instructions: In this task, you have given an input which is agent's reply, based on that you have to identify what will be users' command for that reply. The user generally asks a question related to event scheduling or information of the scheduled events. Agent reply based on the available information or do the task as per user command.
Input: It starts 5:00 PM.
Output:
|
What time?
| 3
|
NIv2
|
task1600_smcalflow_sentence_generation
|
zs_opt
|
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] |
TASK DEFINITION: In this task, you will be given a food review in Persian. You have to Classify the sentiment of the reviewer toward the food into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
PROBLEM: به نسبت اندازه گرونن، ولی از تخم مرغ و بلدرچین بهتره
SOLUTION: mixed
PROBLEM: کیفیت قابل قبولی داره و چربیش کمه فقط قیمت میتونست رقابتی تر باشه نسبت به محصولات مشابه
SOLUTION: positive
PROBLEM: این محصول اصلا شنیسل نیست 4 تیکه مرغ درسته بود که یکم پودر سوخاری بهش زدن من برای پخت مجبور شدم هر تیکه را به سه قسمت برش بزنم
SOLUTION:
|
very negative
| 8
|
NIv2
|
task527_parsinlu_food_overal_classification
|
fs_opt
|
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] |
Part 1. Definition
In this task, you are given two sentences in Persian separated with <sep>, and you have to determine whether the sentences are paraphrases or not. Classify your answers into "paraphrase" and "not-paraphrase".
Part 2. Example
اگر Apple ID و ایمیل خود را فراموش کرده ام چه کاری می توانم انجام دهم؟ <sep> Apple ID و رمز عبور خود را فراموش کردم ، چه کاری باید انجام دهم؟
Answer: not-paraphrase
Explanation: This is a good example. The first sentence is about forgetting email, and the second one is about forgetting the password, so they are not paraphrases.
Part 3. Exercise
برای خندیدن کودک شما چه کاری انجام می دهید؟ <sep> روشی مطمئن برای خندیدن کودک شما چیست؟
Answer:
|
paraphrase
| 7
|
NIv2
|
task465_parsinlu_qqp_classification
|
fs_opt
|
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] |
Detailed Instructions: A text is given in Bengali. Translate it from the Bengali language to the Marathi language. The translation must not omit or add information to the original sentence.
See one example below:
Problem: স্তন্যপান সম্পর্কে অভিভাবকদের সচেতন ও উৎসাহদানে গুরুত্ব বিশ্ব স্তন্যপান সপ্তাহ পয়লা থেকে ৭ই আগস্ট
Solution: 7 ऑगस्टपासून जागतिक सक्शन आठवड्यात सक्शन आणि प्रोत्साहित करणार्या पालकांचे महत्त्व
Explanation: Correct translation for given sentence. Input sentence means 'The importance of the parents about the suction and encouraging the importance of world suction weeks from 7th August' which is the same as the output sentence.
Problem: এই ক্ষেত্রে আমাদের অভিন্ন লক্ষ্য এবং অনুসন্ধিৎসার প্রতিফলন ঘটেছে এই সম্মেলনে ৬০টিরও দেশের ৭ হাজার প্রতিনিধির উপস্হিতির মধ্যে।
Solution:
|
आज इथे जमलेले 60 हून अधिक देशांचे सात हजारपेक्षा जास्त प्रतिनिधी ह्याच सामाईक इच्छेची परिणती आहे.
| 4
|
NIv2
|
task1061_pib_translation_bengali_marathi
|
fs_opt
|
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] |
You're given a sentence and your task is to classify whether the sentence is acceptable or not. Any sentence which is grammatically correct, has a naturalistic text, is written by a native speaker and which minimizes superfluous content is acceptable, otherwise unacceptable. If the sentence is acceptable then write "acceptable", otherwise "unacceptable".
The reporters expected that the principal would fire some teacher.
|
acceptable
| 0
|
NIv2
|
task616_cola_classification
|
zs_opt
|
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] |
Given a sentence in the Japanese and Central Khmer language. Your task is check if the Khamer sentence is translation of Japanese. if the translation is correct than generate label "Yes", otherwise generate label "No".
Example: Japanese: 詳細は昨日UTC17時30分、英国議会でイギリスのルス・ケリー運輸大臣によって伝えられた。
Khamer: បណ្តាអ្នកនយោបាយនៅចក្រភពអង់គ្លេសបន្តលើកកង្វល់អំពីការបាត់បង់ ជាពិសេសព័ត៌មាន ព្រមទាំងសមត្ថភាពរដ្ឋាភិបាលក្នុងការសុវត្ថិភាពនិងប្រមូលរក្សាទិន្ន័យ។
Example solution: Yes
Example explanation: The converted Khamer sentence is correctly translated from Japanese because converted sentence has the same message as the original Japanese sentence that Details were given yesterday in the House of Commons at 1730 UTC by Britain's Transport Secretary Ruth Kelly.
Problem: Japanese: マスコミ報道は、チームのパフォーマンスや国際的なランキングと相関させるべきでしょうか？
Khamer: តើប្រព័ន្ធផ្សព្វផ្សាយ មានទំនាក់ទំនងទៅនឹងការប្រតិបត្តិរបស់ក្រុមនិងចំណាត់ថ្នាក់អន្តរជាតិដែរឬទេ?
|
Solution: Yes
| 5
|
NIv2
|
task1123_alt_ja_khm_answer_generation
|
fs_opt
|
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] |
Instructions: In this task, you are given a review of a movie and a boolean question whether this review has positive sentiment or negative sentiment. Your task is to generate answer "yes" when the tweet has that particular sentiment, otherwise generate answer "no".
Input: Review: This was a truly bad film. The character "Cole" played by Michael Moriarty was the biggest reason this flopped, the actor felt that conjuring up an unbelievably awkward southern drawl would make this character more evil, it didn't. After about 20 minutes I had wished for a speech therapist to make an appearance, this would have added some sincerity.- 1) badly acted - 2) unsympathetic characters - 3) razor thin plot lineYuck! Question: is it a negative review?
Output:
|
yes
| 3
|
NIv2
|
task285_imdb_answer_generation
|
zs_opt
|
[
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] |
You will be given a definition of a task first, then some input of the task.
In this task, you're given a statement, further information available on a particular linked term from the statement, and a question. Your job is to generate the answer to the question by using the information provided. If there is no clear answer obtainable, output 'none'.
Context: He also presided over the less successful committee when Bolivia and Paraguay went to war over the Gran Chaco. Link Information: The Chaco War (1932–1935; , ) was fought between Bolivia and Paraguay over control of the northern part of the Gran Chaco region (known in Spanish as Chaco Boreal) of South America, which was thought to be rich in oil. Question: What year did Bolivia and Paraguay go to war over the Gran Chaco?
Output:
|
Answer: 1932
| 1
|
NIv2
|
task237_iirc_answer_from_subtext_answer_generation
|
zs_opt
|
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] |
Detailed Instructions: You are given a sentence in Spanish. Your job is to translate the Spanish sentence into Hebrew.
Problem:Hace pocas semanas empacamos el SkySat 1, pusimos nuestras firmas en él y le despedimos por última vez en la Tierra.
Solution:
|
לפני מספר שבועות, ארזנו את הלוויין SkySat 1, חתמנו את שמותינו עליו ונפרדנו ממנו לשלום לפני שיעזוב את כדור הארץ.
| 8
|
NIv2
|
task1229_ted_translation_es_he
|
zs_opt
|
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] |
In this task, you are given a sentence in the English language and your task is to convert it into the Japanese language. In translation, keep numbers as it is and make it sentence case (capitalize only the first word of each sentence and noun).
One example: Italy have defeated Portugal 31-5 in Pool C of the 2007 Rugby World Cup at Parc des Princes, Paris, France.
Solution is here: フランスのパリ、パルク・デ・プランスで行われた2007年ラグビーワールドカップのプールCで、イタリアは31対5でポルトガルを下した。
Explanation: This is a correct and accurate translation from English to Japanese because translated text is just paraphrased of English sentence. Also, it preserves the numbers as it is.
Now, solve this: The plaintiffs in the suit included adherents of varying religious backgrounds, including Christianity and Hinduism.
Solution:
|
訴訟の原告には、キリスト教やヒンズー教から改宗した人もいた。
| 6
|
NIv2
|
task435_alt_en_ja_translation
|
fs_opt
|
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] |
Given the task definition and input, reply with output. You are given a conversation between two people. 'Person1:' and 'Person2:' are used to separate their respective dialogues. Your task is to label each of the continuous conversations done by one speaker with the emotion detected in that particular part. Your labels can be one among the following: 'No emotion', 'surprise', 'happiness', 'sadness', 'anger', 'disgust'.
Person1: Are you interested in mountaineering ?
Person2: Yes . in some sense . I should say I like mountain climbing , you know , because I'm not so professional . How about you ?
Person1: I'm the same with you , an amateur mountain climber . 1 went climbing in the Rocky Mountains with my friends last summer vacation . We spent two days in the mountains .
Person2: How did you spend the night there ?
Person1: We pitched a tent in the night , waiting for the sunrise . Wow ! It was too beautiful .
Person2: I had a similar experience , but that was on the top of Tasha Mountain in my country .
Person1: Tasha Mountain ? It's very famous in China , isn't it ?
Person2: Yes.It IS one of the Five Mountains .
Person1: I want to climb Tasha Mountain this summer vacation . Would you be my guide ?
Person2: I'd love to .
|
No emotion,No emotion,No emotion,No emotion,happiness,No emotion,No emotion,No emotion,No emotion,happiness
| 5
|
NIv2
|
task1532_daily_dialog_emotion_classification
|
zs_opt
|
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] |
You are given a sentence in Galician. Your job is to translate the Galician sentence into Arabic.
Example: É basicamente un caldeiro que rota.
Example solution: ويوجد أساسا مرجل دوار.
Example explanation: The Galician sentence is correctly translated into Arabic, because the meaning is preserved.
Problem: É coma se os teus ollos comenzaran a forzarse para verse a si mesmos.
|
Solution: وهي مثل عيونكم تحملق محاولة رؤية نفسها.
| 5
|
NIv2
|
task1241_ted_translation_gl_ar
|
fs_opt
|
[
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] |
In this task, you will be shown a Persian passage and a question, and you have to determine whether the question is answerable based on the passage or not. If the question is answerable, choose the "True" label, and if not select "False"
Q: نخستین سه تمدن جهان، بر اساس زمان آغاز آنها، بینالنهرین، مصر و هند هستند که به تمدنهای مرده، مشهور بوده و تمدن غربی، از همین سه تمدن، نشات گرفتهاست. تمدن چین، استثنای خارقالعادهای میباشد. انقلاب صنعتی، به عنوان محرکی نیرومند، با ایجاد نهادهای اقتصادی، اداری، سیاسی، اجتماعی و فرهنگی، ساختار شهرها را دگرگون کرد. بلکه تغییرات و تحولات شگرفی را در حیات اجتماعی شهر، به وجود آورد. شهر، مکانی با تراکم بالای جمعیت و مرکزیت سیاسی، اداری و تاریخی است که در آن، فعالیت اصلی مردم، غیر کشاورزی است و دارای مختصات شهری بوده که از طریق دولتی محلی، اداره میشود. شهر، محلی است با حدود قانونی که در محدوده جغرافیائی بخش، واقع شده و از نظر بافت ساختمانی، اشتغال و سایر عوامل، دارای سیمایی با ویژگیهای خاص خود، بوده؛ بهطوریکه اکثریت ساکنان دائمی آن، در مشاغل کسب، تجارت، صنعت، کشاورزی، خدمات و فعالیتهای اداری، اشتغال داشته و در زمینه خدمات شهری، از خودکفائی نسبی، برخوردار و کانون مبادلات اجتماعی، اقتصادی، فرهنگی و سیاسی حوزه جذب و نفوذ پیرامون خود، بوده و حداقل، دارای ده هزار نفر، جمعیت باشد. در زبان فارسی باستان، شهر را خشت میگفتند که به معنی پادشاهی بودهاست. در اوستا این واژه بهصورت خَشَتَر بوده که واژهٔ شهر در فارسی نو از همین واژهٔ اوستایی آمدهاست. شهرها، خاصیت مشترکی دارند و آن، تمرکز افراد در یک فضا، برای برآوردن خواستههای مشترک شان است؛ بنابراین، تفاوت خواستههای مشترک افراد، در دوران گوناگون تاریخ است که باعث پیدایش نظریههای مختلف، دربارهٔ دلایل به وجود آمدن شهر، شدهاست. عمدهترین اثر انسان بر روی کره خاکی ایجاد مجتمعهای زیستی بالاخص شهرها میباشد. چرا که اغلب آثار به دلیل وجود شهرها و گسترش انهاست که به وجود میآیند و رشد میکنند. هنگامی که شهری گسترش مییابد تا به شهر دیگری میرسد، این منطقه شهری میتواند شهری مهم و گاهی، ابر شهر مرکز جهانی، قلمداد شود.
Question: اسم شهر در ایران قدیم چی بود؟
A:
|
True
| 4
|
NIv2
|
task396_persianqa_classification
|
zs_opt
|
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] |
In this task you will be given an arithmetic operation and you have to find its answer. The symbols of operators '+' and '-' has been swapped i.e you need to perform subtraction when you see a '+' symbol and addition in case of '-' symbol.
[Q]: 3394 - 2316 - 9713
[A]: 15423
[Q]: 4377 + 5604 - 8336 - 2652
[A]: 9761
[Q]: 8625 + 2916 - 3947
[A]:
|
9656
| 5
|
NIv2
|
task085_unnatural_addsub_arithmetic
|
fs_opt
|
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] |
This task is to translate the Telugu Language Input to English Language Output
Q: మాక్స్. వ్యవస్థ ఒత్తిడి (MPa) 18.6
A:
|
Max. system pressure (MPa) 18.6
| 4
|
NIv2
|
task1617_cc_alligned_translate_tel_eng
|
zs_opt
|
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] |
Detailed Instructions: You are given a sentence in Persian. Your job is to translate the Farsi sentence into English.
Problem:من عاشق ادبیات انگلیسی بودم ، و وقتی کالج بودم شک داشتم که کدوم را انتخاب کنم ؟ من عاشق ادبیات انگلیسی بودم ، و وقتی کالج بودم شک داشتم که کدوم را انتخاب کنم ؟
Solution:
|
Well I used to love English literature, and I debated in college, which one should I do?
| 8
|
NIv2
|
task1265_ted_translation_fa_en
|
zs_opt
|
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] |
Detailed Instructions: In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional information about the situation that might weaken or strengthen the hypothesis. A weakener is a statement that weakens the hypothesis, that is, it makes you much less likely to believe the hypothesis is true. A strengthener is a statement that strengthens the hypothesis, that is, it makes you much more likely to believe the hypothesis is true. Your task is to answer with 'strengthener' or 'weakener' if the update strengthens or weakens the hypothesis, respectively.
Q: Premise: A person in a blue kayak battles the waves.
Hypothesis: A woman is in a kayak in the ocean.
Update: The kayak is on lake Michigan
A:
|
weakener
| 9
|
NIv2
|
task936_defeasible_nli_snli_classification
|
zs_opt
|
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] |
You will be given a definition of a task first, then some input of the task.
This task is to identify the language of a sentence correctly by classifying if it is English or Telugu
123456 Next > >> Page 1 / 27
Output:
|
English
| 1
|
NIv2
|
task1618_cc_alligned_classify_tel_eng
|
zs_opt
|
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] |
Teacher:In this task, you're given the title of a five-sentence story, the first four sentences, and two options for the fifth sentence as a and b. Your job is to pick the sentence option that does not connect with the rest of the story, indicating your choice as 'a' or 'b'. If both sentences are plausible, pick the one that makes less sense.
Teacher: Now, understand the problem? Solve this instance: Title: Near Steal. Sentence 1: Kate was taking the bus home from work. Sentence 2: She was waiting at the stop. Sentence 3: A shady character walked near her. Sentence 4: She clutched her purse tightly. Choices: a. He walked by without getting it. b. I was disappointed, but I found out my parents bought it for me.
Student:
|
b
| 6
|
NIv2
|
task214_rocstories_incorrect_ending_classification
|
zs_opt
|
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] |
Part 1. Definition
In this task, you're given a fill-in-the-blank question that contains two object names. Additionally, you're given one answer which is one of the objects present in the question. In this task, you need to minimally change the given question so that the answer flips to another object in the question. Remember the question has to be about both objects which are related but different; for example, "trophy" and "suitcase". This task typically involves replacing one word i.e., the 'trigger word' with its antonym (e.g., changing from "small" to "big"). You should not change any content in the given question beyond a word or two, i.e., the trigger word/phrase. The expected answer to your question must not be associated with just the trigger word; instead, it should depend on the context present in the question. The expected answer should not be ambiguous. For your question, there should be an agreed upon answer to fill in the blank. Your generations should NOT contain potentially explicit, offensive, or adult content. In your generated question, retain the object names from the input question. Avoid repeating the same style or phrase in generating your modified question e.g. this task can be always solved using a simple negation i.e. by adding not, never, etc. Instead, try to increase the word diversity. Your question must contain at least 15 and at most 30 words. Your question must have at least 70% overlapping words with the given question. You must utilize the given context word while writing the question. Your question must contain only one blank. The two objects should be used ONCE in your question. Here is a list of attributes and associated contrastive words that may help write contrastive trigger words. Note that contrastive trigger words help in flipping the label.
| Attribute | triggerword | contrastive triggerword |
| age | old | new |
| altitude | low | high |
| area | small | vast |
| brightness | dark | light |
| clarity | obscure | clear |
| cleanness | dirty | clean |
| complexity | simple | complex |
| cost | cheap | expensive |
| density | sparse | dense |
| depth | shallow | deep |
| distance | near | far |
| electric conductivity | low | high |
| flexibility | rigid | flexible |
| granularity | fine | coarse |
| hardness | soft | hard |
| length | short | long |
| magnitude | small | large |
| mass | small | large |
| odor | weak | strong |
| pressure | low | high |
| resistance | low | high |
| shape | round | sharp |
| shape | flat | spiky |
| size | small | large |
| sound | quiet | loud |
| sound pitch | low | high |
| speed | slow | fast |
| stability | unstable | stable |
| strength | weak | strong |
| temperature | low | high |
| texture | smooth | rough |
| thermal conductivity | low | high |
| thickness | thin | thick |
| volume | small | large |
| weight | light | heavy |
| width | narrow | wide |
| location | in | out |
| location | up | down |
| location | above | below |
| location | on | off |
| location | to | from |
Part 2. Example
Context word: fit.
Question: The trophy doesn't fit into the brown suitcase because _ is too large.
Answer: trophy.
Answer: The trophy doesn't fit into the brown suitcase because _ is too small.
Explanation: Here, replacing the trigger word "large" by its antonym "small" flips answer to the second object ("suitcase") in the question. So, this is a valid question.
Part 3. Exercise
Context Word: goal.
Question: After setting a goal of losing ten pounds, Brenda chose the yogurt instead of the doughnut, because the _ has fewer calories.
Answer: yogurt.
Answer:
|
After setting a goal of losing ten pounds, Brenda chose the yogurt instead of the doughnut, because the _ has more calories.
| 7
|
NIv2
|
task034_winogrande_question_modification_object
|
fs_opt
|
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] |
Detailed Instructions: In this task, you are given an input list A comprising of numbers and alphabets. You need to extract and sort the unique alphabets in the list. The alphabets in the input list will only be in lowercase. Return -1 if there is no alphabet in the input list.
Q: ['9319', '1089', '4199', '9925', '3041', '5149', '2969', 'k', 'u', 'j', 'd', '9399', '699', '1701', 'c']
A:
|
c, d, j, k, u
| 9
|
NIv2
|
task636_extract_and_sort_unique_alphabets_in_a_list
|
zs_opt
|
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0.8424056768417358
] |
In this task, you are given an input list A. You need to find all the elements of the list that are alphabets in the same order as they appear in the list A. Do not change their case/capitalization.
Example Input: ['6119', 'G', '9025', '73', 'o', 'Z', 'n', 'a', '6929', 'w', '2257', '1847', 'P', 'd', 'r', '283', 'u', 'C', 'F', '3393', '541', 'B']
Example Output: G, o, Z, n, a, w, P, d, r, u, C, F, B
Example Input: ['2485', '3069', '9879', '5231', 'x', '9407', 'H', '7143', 'D', '7819', 'z', 'h', 'J']
Example Output: x, H, D, z, h, J
Example Input: ['V', '4857', '4391', '603', '8189', 'K', 't', 'x', 'z', '2249', '1593', '7395', '6621', 'p', '9029', 'S', 'Y', 'f', 'B', '4321', 'B', '8029', 'k', 'D', 'b', '2339', 'e', '8415']
Example Output:
|
V, K, t, x, z, p, S, Y, f, B, B, k, D, b, e
| 3
|
NIv2
|
task488_extract_all_alphabetical_elements_from_list_in_order
|
fs_opt
|
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] |
Given a command in a limited form of natural language, provide the correct sequence of actions that executes the command to thus navigate an agent in its environment. A command can be broken down into many different actions. Actions are uppercase and are individual steps that serve as the building blocks for a command. For commands, 'left' and 'right' are used to denote the direction of an action. The word 'opposite' turns the agent backward in the specified direction. The word 'around' makes the agent execute an action while turning around in the specified direction. The word 'and' means to execute the next scope of the command following the previous scope of the command. The word 'after' signifies to execute the previous scope of the command following the next scope of the command. The words 'twice' and 'thrice' trigger repetition of a command that they scope over two times or three times, respectively. There are only six actions: 'I_LOOK', 'I_WALK', 'I_RUN', 'I_JUMP', 'I_TURN_LEFT', and 'I_TURN_RIGHT'. These actions respectively align with the commands 'look', 'walk', 'run', 'jump', 'turn left', and 'turn right'. Actions and commands do not have quotations in the input and output.
Ex Input:
run around right thrice after walk around left
Ex Output:
I_TURN_LEFT I_WALK I_TURN_LEFT I_WALK I_TURN_LEFT I_WALK I_TURN_LEFT I_WALK I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN I_TURN_RIGHT I_RUN
Ex Input:
turn opposite left and walk around right thrice
Ex Output:
I_TURN_LEFT I_TURN_LEFT I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK I_TURN_RIGHT I_WALK
Ex Input:
look around right twice after jump around right thrice
Ex Output:
|
I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK I_TURN_RIGHT I_LOOK
| 1
|
NIv2
|
task130_scan_structured_text_generation_command_action_long
|
fs_opt
|
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] |
Detailed Instructions: Languages typically provide more than one grammatical construction to express certain types of messages. Your task is to generate a senetence with the same meaning as given sentence, but with different grammatical construction.
Q: Maria phoned the man who was from work the greetings
A:
|
Maria phoned the greetings to the man who was from work
| 9
|
NIv2
|
task132_dais_text_modification
|
zs_opt
|
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Definition: This task is to translate a news commentary given in Arabic language into Czech language. Translate the input statement into the output language while preserving the numberical values, special characters and context of the commentary done.
Input: ليس من المستغرب أن يسارع عدد كبير من هيئات الأمم المتحدة ووكالات المعونة في البلدان الغنية إلى مقاومة هذا التوجه. فكما هي الحال غالباً، يتحول الأمر إلى صراع على مناطق النفوذ، بدلاً من التنافس من أجل تقديم أكثر السبل كفاءة وفعالية للتعجيل بتقديم المساعدة إلى الفقراء. وعلى هذا فإن أوباما ، و رود ، و ثاباتيرو ، وغيرهم من زعماء الفكر التقدمي قادرون على إحداث تغيير هائل من خلال متابعة تنفيذ التعهدات التي بذلوها في إطار مجموعة الثماني، والإصرار على إنجاح المساعدات حقاً. أما الجهات البيروقراطية فلابد من تجاوزها من أجل التعجيل بحمل المساعدات إلى حيث يحتاج الناس إليها حقاً: إلى التربة التي تحرثها أفقر الأسر المزارعة في العالم.
Output:
|
Budou-li se Obama, Rudd, Zapatero a další lídři s\xa0myšlením upřeným do budoucna závazky ze summitu G8 nadále zabývat a budou-li trvat na tom, aby pomoc skutečně fungovala, mohou tudíž dokázat velké věci. Je nutné obejít byrokratické aparáty, aby se pomoc dostala tam, kde je jí třeba: na půdu obdělávanou nejchudšími rolnickými rodinami na světě.
| 2
|
NIv2
|
task1374_newscomm_translation
|
zs_opt
|
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] |
Given a sentence, fill out the missing word with a 'no' or a number (between zero and ten). You should write the numbers with english alphabet, like: four instead of 4.
Most children stop wetting the bed at night by the time they are ____ or five.
|
four
| 0
|
NIv2
|
task1359_numer_sense_answer_generation
|
zs_opt
|
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] |
Given a sentence in Italian, generate a new Italian sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
[Q]: Il foglio di calcolo è popolato circa di per lo più studenti.
[A]: L'Oceano ha una forma chimica di gran parte dell'acqua.
[Q]: Puoi usare una riga per indicare il posto insieme alla colonna.
[A]: Puoi usare una tastiera per indicare il posto insieme alla colonna.
[Q]: Siete dubbio di trovare un letto in un negozio di alimentari.
[A]:
|
È probabile che si rifornisca un coniglio in un giardino di alimentari.
| 5
|
NIv2
|
task408_mickey_it_sentence_perturbation_generation
|
fs_opt
|
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] |
Given the task definition, example input & output, solve the new input case.
You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Hebrew.
Example: ويمكن اعادة تكرار هذه العملية على طول الشريط لكي نطبق شريط الحمض النووي فنشكل ما يشبه المستطيلات
Output: סך כל הפעולות של המהדקים על הגדיל הארוך מקפלות אותו לצורה דמויית מלבן.
The Arabic sentence is correctly translated into Hebrew, because the meaning is preserved.
New input case for you: لأننا في الحقيقة نحل مشكلة منطقية جينية أو نسأل سؤالا جينيا منطقيا
Output:
|
כי אנחנו בעצם ניצבים מול בעיה גנאולוגית, שאלה גנאולוגית (על שושלות יוחסין).
| 1
|
NIv2
|
task1233_ted_translation_ar_he
|
fs_opt
|
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In this task, you are given a text of many news articles seperated by special token "|||||". Your task is to summarize them.
An 80-year-old Spanish nun appeared in court Thursday to face charges that she kidnapped an infant girl as part of a vast baby-trafficking ring that stole newborns from poor mothers and sold them into adoption.
Sister Maria Gomez Valbuena appeared before the judge and refused to testify, invoking her right to remain silent, Reuters reported.
The aging Sisters of Charity nun was charged with kidnapping a newborn girl from a Madrid hospital in the 1980s.
A group of more than 1,000 families said she was part of a nationwide baby-snatching ring dating back four decades.
The group, Anadir, said the ring involved hundreds of doctors, nurses, priests, nuns and midwives who schemed to convince poor or unwed mothers their babies had died at birth.
The babies were then sold to families looking to adopt, and the child's birth papers were doctored, the group said.
Lawyers say Catholic priests and nuns acted as middlemen, paying hospitals workers for babies and then selling them to desperate families.
“The nuns and priests justified what they did by saying that the child was better off with the adoptive family, but they still took the money,” Enrique Vila, a lawyer who works for families trying to track down relatives, told The Daily Beast.
Anadir said the racket dates back to the Spanish Civil War, when Francisco Franco’s fascist minions stole babies from political prisoners, Reuters reported.
In later years, it became a money-making scheme for scores of hospitals and clinics, the group said.
Since the 1980s, Spanish police have investigated hundreds of claims from mothers who said their babies were stolen, and their stories have been well-documented in the Spanish media.
In one infamous case, workers at one Madrid clinic kept a dead newborn in the freezer, which they would show to mothers to prove their baby had died, The Daily Beast reported.
Valbuena is the first person to be charged in the scheme.
The case against her began after a woman said the nun intimated her into giving up her baby at Madrid’s Santa Cristina hospita in 1982, The Daily Beast reported.
“I was still half asleep when I asked her where my daughter was,” Marisa Torres Torres told AFP.
“She told me: ‘Stop asking me that or else I will also take away your other daughter and you will go to jail for adultery.’ ”
Torres was reunited with her now 29-year-old daughter, Pilar, last year, and said DNA tests proved they were mother and daughter, the Daily Best reported.
Outside the courthouse on Thursday, a throng of angry mothers and families jeered the nun as she was hustled to a car by police.
Paloma Perez, 55, told the AFP she believed she was stolen from her birth mother at Santa Cristina hospital in 1957.
“It is shameful, and on top of it all they protect her,” Perez said, as Valubeana’s car drove away. “I don’t even know who I am.”
With News Wire Services ||||| article
Did doctors and the Catholic Church traffic in stolen newborns? Mike Elkin reports from Madrid on the shocking trial of one nun.
On his deathbed five years ago in Barcelona, Juan Moreno told his son Juan Luis the family secret: we bought you from a priest in 1969. Moreno Sr. paid 150,000 pesetas, more than what he paid for their apartment seven years earlier. DNA tests have revealed that Juan Luis’s birth certificate, which names his adoptive parents as his birth parents, is false. And Antonio Barroso, Juan Luis’s best friend, was also bought from the same priest, according to Juan Luis. The two families would take summer vacations together to Zaragoza, around 300 kilometers west of Barcelona, but the men now believe the trips’ true purpose was to pay the installments on the two boys to the clergyman, Juan Luis says.
“My adoptive family gave me plenty of love, but it’s also true that my life has been a lie,” Juan Luis says over the telephone from Barcelona.
Moreno is one of thousands of Spaniards who suspect that doctors, nurses, priests, and nuns stole and sold babies between the 1960s and the early 1980s. While the 1,500 open cases vary, a common story has a woman being told her newborn has died shortly after birth. The baby is sold to another couple, and official papers are doctored so this couple appeared as the biological parents. At one infamous Madrid clinic, now closed, called San Ramón, it was reported in the Spanish media that staff kept a dead newborn in the freezer to prove to mothers that their babies had died.
Spanish police investigated a handful of cases in the early 1980s, but only within the last few years has the nationwide scope of the alleged trafficking started to take shape.
On Thursday, a Madrid judge is scheduled to question 80-year-old Sister María Gómez Valbuena on charges of illegal imprisonment (it is called illegal imprisonment instead of kidnapping because under Spanish law kidnapping involves a ransom). The case began when Marisa Torres filed accusations against the nun after meeting her daughter Pilar, now 29, for the first time last year. Torres had gone to Sister María at the Santa Cristina clinic in 1982 after seeing a magazine ad for expectant mothers who needed social assistance. Torres had separated from her husband, and the father of the baby was another man. Torres told a judge last week that after giving birth, Sister María intimidated her into giving up the child. The nun, Torres said, threatened to denounce her to authorities for being an adulteress, saying they would likely take away her 2-year-old daughter. When Pilar and her adoptive father began to investigate her roots, they spoke to Sister María. She told them Pilar’s mother was a prostitute and to forget about her, Pilar said. Sister María hasn’t responded to the charges.
Despite the recent number of cases being investigated, Spanish authorities have largely avoided the issue in the past, taking years to confirm what people had been talking about for decades (Sister María, for example, was named in a story in Interviú magazine about alleged child trafficking at her clinic in 1982). To assist those searching, Moreno and Barroso decided to found Anadir, now the largest association, of around 1,100 people, who are looking for parents, children, and siblings.
“It’s sad that the Spanish justice system is incapable of doing its job,” Moreno says. “We’ve given them false birth certificates, false death certificates, we’ve opened empty coffins, and supplied hundreds of testimonies. What more do they need?” This Thursday, the Spanish justice minister will be meeting with representatives of some of the families to discuss their grievances.
Internet groups have connected searchers across the country. Websites and Facebook pages abound with stories and pleas:
“I’m looking for my biological family. I was adopted in 1961 and my papers are full of irregularities. Thanks and good luck to everyone.”
“I’m searching for my brother or sister, born in Bilbao on January 27, 1972. They took the baby away right after the birth. Strange things happened, and a few hours later they told my mother that the baby had died. They refused to show it to her.”
“I’m looking for my brother who was born on October 11, 1964 in Málaga and was said to have died four days later, but it doesn’t appear that he was buried. We know that the dates and places of birth are not reliable.”
Courts are now looking into baby-trafficking cases in Valencia, the Basque Country, Madrid, Catalonia, Andalusia, and the Canary Islands.
“I view this baby-stealing network like a mafia,” says Enrique Vila, a Valencia-based lawyer who works on behalf of the five associations that provide support for people looking for their biological families. “If this wasn’t organized, how did babies born in Valencia end up in Galicia, around 1,000 kilometers away?” The Catholic Church in Spain has declined to respond to the allegations, and declined to talk to The Daily Beast for this story. Vila, who was adopted and suspects he might have been taken from his biological mother, said that he believes hospital staff perpetrated these thefts for money and the church acted as a distribution network. “The nuns and priests justified what they did by saying that the child was better off with the adoptive family, but they still took the money,” he said.
Even legal adoptions signed by a judge and sealed by a notary have been found to contain false information, as revealed in numerous investigations brought by adopted children and their families. So there is a heightened sense of uncertainty among Spanish adoptees. Last year in Barcelona, during the first meeting between a mother and her 40-year-old daughter, the latter read her own death certificate.
With nothing to trust, Spaniards are erring on the side of suspicion. But sometimes the trails don’t lead to trafficking. Last year a woman from León, Spain, whose twin sister supposedly died at birth, began to question the official story after reading about other cases in the press. On the Internet she found a woman who shared the same age and an uncanny resemblance. DNA tests, however, came back negative. Likewise, a few recent grave exhumations and subsequent DNA tests showed no wrongdoing. Nevertheless, other exhumations have revealed coffins filled with rocks, an adult femur, and the bones of another baby, according to various reports in the Spanish media.
Allegations of church-condoned baby trafficking are not confined to Spain. Investigations by the press and the government into similar allegations in Australia found that Catholic hospitals from the 1950s to 1970s coerced women using drugs to sign adoption papers or to simply knock them out long enough to whisk the baby away. Catholic Health Australia, the largest nongovernment health provider in the country, has formally apologized for what happened.
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The effects of the Spanish trafficking have reached the United States as well. Austin-based Randy Ryder thought he was born in the Spanish city of Málaga to his Austrian mother and American father, but the truth came out 12 years ago. Last October, Ryder appeared in a BBC report because a Spanish woman believed him to be her lost brother. DNA tests proved negative, but his biological mother, who now lives in London, saw the show and reached out to him on Facebook. This time, the DNA matched.
New Yorker Maria Washbourn was born in Spain in 1961, but through the church and an intermediary was adopted by an American couple. A friend noticed a physical resemblance between her and a Madrid man, Vicente González Olaya, who writes a blog about the search for his missing older sister. “It’s a long-shot,” says González, who also covers the auto industry for El País newspaper, “but we’re waiting for the DNA results to come back.”
González has kept an online record of his investigation, an often surreal account of dealing with the church, hospitals, DNA labs, and Spain’s vast bureaucracy of registrars and archives.
The nuns, he said, told his devoutly Catholic mother that the baby had choked to death on the umbilical cord, even though his mother saw the child alive. After his parents died early last year, González started to make some inquiries. Nothing made sense. |||||
|
An 80-year-old nun has been busted in Spain for her role in a massive Catholic church-linked baby-theft ring, according to investigators. The ring took babies from hundreds of poor or unwed mothers dating back to the 1980s and sold them into adoption, say officials. Sister Maria Gomez Valbuena, the first person to be arrested in the operation, has been charged with snatching a newborn from a young mom in Madrid in 1982. When the mother demanded to know where her baby was, the nun "told me: ‘Stop asking me that or else I will also take away your other daughter and you will go to jail for adultery,'" said the mom, who has been reunited with her 29-year-old daughter. Valbuena appeared before a judge yesterday and refused to testify, reports the New York Daily News. Police have received hundred of complaints of baby snatchings, which have been widely investigated by the media. Catholic priests and nuns served as middlemen in the operations that also involved doctors, nurses, lawyers, and scores of clinics and hospitals, according to police investigators. In one case, a Madrid hospital kept a dead newborn in a freezer to show moms to prove their babies had died. “The nuns and priests justified what they did by saying that the child was better off with the adoptive family, but they still took the money," a lawyer for the targeted families told the Daily Beast.
| 0
|
NIv2
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task1291_multi_news_summarization
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zs_opt
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] |
Given the task definition and input, reply with output. You are given a sentence in Spanish. Your job is to translate the Spanish sentence into Polish.
Incluso si no sabes nada sobre el libro, te enfrentas a una persona que lucha entre dos planos de la existencia.
|
Zatem nawet jeśli nie wie się nic o samej książce, jest się zmuszonym do pomyślenia o jednej osobie zawieszonej między dwoma płaszczyznami istnienia.
| 5
|
NIv2
|
task1102_ted_translation_es_pl
|
zs_opt
|
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Given a sentence in the Japanese and Indonesian(Bahasa variant) language. Your task is check if the Bahasa Indonesia sentence is translation of Japanese. if the translation is correct than generate label "Yes", otherwise generate label "No".
--------
Question: Japanese: 香港は香港基本法の一環として、「ひとつの国、ふたつのシステム」という中国との間の合意の下、西洋式の自由を享受し続けている。
Bahasa Indonesia: Momentum yang tumbuh disekitar Konvensi Kerangka Kerja WHO pada Kontrol Tembakau ini sepertinya sulit untuk dihentikan.
Answer: No
Question: Japanese: 警察は、目撃者の証言が狙撃者はその後被害者を車で轢き、E.Uticaを西に高速で走り去ったことを示していると言っている。
Bahasa Indonesia: Polisi mengatakan bahwa laporan saksi mata menunjukkan penembak lari melewati korban dengan mobil dan melesat pergi ke arah Westbound di E. Utica.
Answer: Yes
Question: Japanese: 地方自治体は灰の層が被災地のコーヒーの木を燃やし、この地域のコーヒー生産を相当減少させると予想している。
Bahasa Indonesia: Stewart naik 1 tingkat setelah mencapai garis finish di urutan kedua dan berada di posisi kesembilan, sedangkan Greg Biffle jatuh ke urutan kesepuluh.
Answer:
|
No
| 7
|
NIv2
|
task1117_alt_ja_id_answer_generation
|
fs_opt
|
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] |
You are given a sentence in Hebrew. Your job is to translate the Hebrew sentence into Galician.
Q: בפעם הראשונה, צרכנים אמריקניים יראו את שרשרת האספקה של הנפט עומדת מולם.
A:
|
Por vez primeira os consumidores estadounidenses verán en fronte deles a cadea de subministración de petróleo.
| 4
|
NIv2
|
task1110_ted_translation_he_gl
|
zs_opt
|
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] |
In this task, you are given a text of the article. Your task is to generate a headline (title) for this article.
--------
Question: confidence in greek assets sank to a new low monday , as chancellor angela merkel of germany kept up the pressure on greece , insisting on tougher austerity measures .
Answer: confidence in greek debt sinks again matthew saltmarsh reported from paris
Question: on monday , less than three weeks before the official start of the hurricane season , government forecasters predicted an active season that could be comparable to last year 's destructive one .
Answer: forecasters predict an active hurricane season
Question: economic growth in toronto will suffer this year because of sars , a think tank said friday as health authorities insisted the illness was under control in canada 's largest city .
Answer:
|
sars toll on toronto economy estimated at c$ # billion UNK us$ ### million with bc-as-gen world-sars virus
| 7
|
NIv2
|
task288_gigaword_summarization
|
fs_opt
|
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] |
In this task, you need to reverse all words of a given length in the sentence. The number of letters in a word determine its length. For example, the length of the word "apple" is 5.
[Q]: Sentence: 'a green apple and knife on dark wooden stairs'. Reverse all words of length '3' in the given sentence.
[A]: a green apple dna knife on dark wooden stairs
[Q]: Sentence: 'woman holding an umbrella and a small cat'. Reverse all words of length '7' in the given sentence.
[A]: woman gnidloh an umbrella and a small cat
[Q]: Sentence: 'a bed and a desk in a room'. Reverse all words of length '2' in the given sentence.
[A]:
|
a bed and a desk ni a room
| 5
|
NIv2
|
task378_reverse_words_of_given_length
|
fs_opt
|
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] |
Instructions: In this task, you are given a sentence in the English and Hindi language. Your task is check if the Hindi sentence is translation of English. if the translation is correct than generate label "Yes", otherwise generate label "No".
Input: English: Boris Johnson, an English MP writing in The Times in London, has welcomed Muslim opposition in Britain to the events in Sudan and called for muslims to publicly demonstrate their outrage: : "the voices we need to hear now belong to Britain's vast, sensible Muslim majority..."
Hindi: लंदन में द टाइम्स में एक अंग्रेजी सांसद, बोरिस जॉनसन ने लिखा कि सूडान में हुई घटनाओं के लिए ब्रिटेन में मुस्लिम विरोध का स्वागत है और मुसलमानों को सार्वजनिक रूप से अपने आक्रोश को प्रदर्शित करने का आह्वान किया: : "अब हमें जिन आवाज़ों को सुनने की ज़रूरत है, वे ब्रिटेन के विशाल, संवेदनशील मुस्लिम बहुसंख्यक से संबंधित हैं।.."
Output:
|
Yes
| 3
|
NIv2
|
task434_alt_en_hi_answer_generation
|
zs_opt
|
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0.23478484153747559,
-1.1662142276763916,
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-0.749845027923584,
-1.4876453876495361,
-0.39868319034576416
] |
Q: In this task you will be given two lists of numbers and you need to calculate the intersection between these two lists. The intersection between two lists is another list where every element is common between the two original lists. If there are no elements in the intersection, answer with an empty list. Your list of numbers must be inside brackets. Sort the numbers in your answer in an ascending order, that is, no matter what the order of the numbers in the lists is, you should put them in your answer in an ascending order.
[6, 2, 2, 10, 3, 7, 8] , [2, 7, 5, 8, 8, 9, 8]
A:
|
[2, 7, 8]
| 7
|
NIv2
|
task098_conala_list_intersection
|
zs_opt
|
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] |
Definition: In this task, you need to answer the given multiple-choice question on the gain. Gain is the value by which to multiply the input. Classify your answers into 'a', 'b', 'c', 'd', and 'e'.
Input: Problem: seed mixture x is 40 percent ryegrass and 60 percent bluegrass by weight ; seed mixture y is 25 percent ryegrass and 75 % fescue . if a mixture of x and y contains 30 % ryegrass , what percent of the weight of the mixture is x ?
Options: a ) 10 % , b ) 33 1 / 3 % , c ) 40 % , d ) 50 % , e ) 66 2 / 3 %
Output:
|
b
| 2
|
NIv2
|
task1419_mathqa_gain
|
zs_opt
|
[
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] |
In this task, you will be shown a conversation and a question. You need to write an implausible answer to the question. Even though there exist multiple wrong answers, we only need a single wrong answer. "W" and "M" in the conversations stand for "woman" and "man".
Q: M: Hello, Mary. Why are you standing here? W: I'm waiting for a bus. The buses are so full at this time of the day. M: Sure. Where are you going? I don't think this is your way home. W: You are right. I'm going for a walk in the park. M: Going for a walk even after along day's work? W: Yes. I always enjoy walking alone in the park after work. M: I see. Then why not go there on foot? It's not so far from here. W: Oh, no. I hate walking through the streets., Question: When does the talk most likely take place?
A:
|
Early in the morning.
| 4
|
NIv2
|
task283_dream_incorrect_answer_generation
|
zs_opt
|
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] |
Given the task definition, example input & output, solve the new input case.
Given an Amazon customer review, write a title for the review. The preferred titles are under fifteen words.
Example: I was very surprised at the high quality of the stitching, the sturdiness of the handles and the padding for my laptop. The price is amazingly low and the look is very good. I am quite happy with this purchase. It fit my MacBook Pro perfectly, with a little bit of room to spare.
Output: Pleasantly surprised
The review is positive and the user liked the product very much based on the given review, and the title correctly resembles to review.
New input case for you: Horrible, the windows was a fake on it.
Output:
|
One Star
| 1
|
NIv2
|
task1342_amazon_us_reviews_title
|
fs_opt
|
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] |
Definition: Given a sentence in English language, translate the sentence to Igbo language keeping the meaning of the original sentence intact
Input: English sentence: As a result of this, they decided to cook beans while they neglect repairing the roof but when the the rain started heavily, they were forced to repair the roof with the remaining palm fond.
Output:
|
Dị ka a n'ihi nke a, ha kpebiri esi nri agwa mgbe ha na-eleghara anya ịrụzi elu ụlọ ma mgbe mmiri ozuzo malitere kpamkpam, a manyere ha ịrụzi elu ụlọ na ndị fọdụrụ n'ọbụ aka aka ụtọ.
| 2
|
NIv2
|
task452_opus_paracrawl_en_ig_translation
|
zs_opt
|
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] |
Detailed Instructions: In this task, you will be shown an extract from a movie plot and a question. You need to provide the correct answer for it. Short answers containing words that are present in the passage are preferred.
Q: Emma has a strong aversion towards her familys new house, especially the attic. After moving in, she becomes miserable and reclusive. The rest of her family also seems unhappy and unsettled. The situation escalates one day when Emma is in the attic alone. All of a sudden someone who looks like Emmas identical twin sister attacks her viciously.Emma is convinced that someone or something is haunting her, and she refuses to leave her house until she can piece the puzzle together, with the assistance of John Trevor, a sympathetic detective. Eventually, Emma suspects her parents of hiding skeletons in the closet from the familys past and practicing Dark Magick Rituals using symbols Wicca seemingly stole from satanism. As the clues pile up, she discovers that she did once have a sister named Beth, who died twelve days after Emma was born. Emma realizes that this identical twin apparition may actually be Beth returning to life. Emma witnesses Beth killing her brother and the police suspect it was really Emma. John Trevor comes to the rescue when Beth is trying to strangle Emma to death, leaving her with a gun. She suspects that her father was to blame and shoots both her parents. When the police arrive, Emma threatens "John Trevor" who claims to be a paramedic with the gun he gave her. As she points the gun at Trevor, she is really pointing the gun at herself and pulls the trigger. The police later discuss about the incident and Emma's former psychiatrist reveals that Beth and Trevor were only in her mind. Another officer mentions that the previous owner of the house mysteriously died. The psychiatrist says that houses do not kill people, but in this case it did. The movie ends with a new family looking to buy the house. As a young girl about Emma's age explores the attic, "John Trevor," a real estate agent called Richard appears behind her and says they will be seeing a lot of each other., Question: How old was Emma when Beth died?
A:
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Answer: twelve days
| 9
|
NIv2
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task194_duorc_answer_generation
|
zs_opt
|
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] |
Teacher: In this task, you are given a short story consisting of exactly 5 sentences where the second sentence is missing. You are given a candidate for the second sentence and you need to identify if the given sentence connects the first sentence with the rest of the story. Indicate your answer by "Yes" if it connects, otherwise "No". Do not generate anything else apart from "Yes" or "No". The given sentence is incorrect if it changes the subsequent storyline, so that at least one of the three subsequent sentences form a consistent story.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Sentence 1: The cashier was counting the dollar bills at her desk.
Sentence 3: Everyone panicked and started to scream.
Sentence 4: The men threatened the people to remain quiet.
Sentence 5: The cashier handed them the cash so they would go away.
Given Sentence 2: Two men rushed into the store and held their guns up.
Solution: Yes
Reason: People panic when somebody holds their guns up. Sentence 2 correctly connects Sentence 1 with rest of the story. So, the answer is 'Yes'.
Now, solve this instance: Sentence 1: Erin had been searching for weeks for her wedding dress.
Sentence 3: The sales lady asked what she liked, and they tried several gowns
Sentence 4: Nothing really popped, and the sales lady brought out one more dress
Sentence 5: Erin felt like a queen in the beautiful gown, and the Dress was found
Given Sentence 2: She saw a new boutique on her way home, and stopped in to look.
Student:
|
Yes
| 2
|
NIv2
|
task066_timetravel_binary_consistency_classification
|
fs_opt
|
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] |
Given the task definition and input, reply with output. You are given a sentence in Japanese. Your job is to translate the Japanese sentence into English.
ここ何年か Kiva から借金する人たちと話してきて起業家精神の何たるかを教えられました
|
Kiva borrowers, as I interviewed them and got to know them over the last few years, have taught me what entrepreneurship is.
| 5
|
NIv2
|
task1222_ted_translation_ja_en
|
zs_opt
|
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] |
Teacher:In this task, you will be presented with an English sentence, and you have to translate it into Arabic.
Teacher: Now, understand the problem? Solve this instance: She wasn't even that good a fuck.
Student:
|
لم تكن أبداً بهذه البراعة فى الفراش
| 6
|
NIv2
|
task651_opus100_en_ar_translation
|
zs_opt
|
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] |
Given a set of four words, generate the category that the words belong to. Words are separated by commas. The possible categories are social gathering, accomodation, physical property, measurement unit, corporate, nutritional value, boats, police punishment, location (proximity), card games, outdoor sport activity, military rank, baby animals, weather, consumer product, animals, boxing terminology, food, chocolate food, bird, type of sandwich, date status, body part, ocean, employment activity, moral characteristic, elements, poker, computers, construction, guitar part, shady activities, physical activity, kitchenware, temperature, type of rigidity, emotional status, season, mode of transportation, window material, activity, emotional display, geographical property, fried food, store status, widespread, aesthetic characteristic, alcoholic drinks, secretary duty, direction, personal characteristic, and animal.
happy, fine, decent, content
|
emotional status
| 0
|
NIv2
|
task143_odd-man-out_classification_generate_category
|
zs_opt
|
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1.1567413806915283,
-0.22876699268817902,
-0.6892498135566711,
-0.32858553528785706
] |
Given the task definition and input, reply with output. In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ': Adjectives are words that typically modify nouns and specify their properties or attributes, 'ADP': Adposition is a cover term for prepositions and postpositions, 'ADV': Adverbs are words that typically modify verbs for such categories as time, place, direction or manner, 'AUX': An auxiliary is a function word that accompanies the lexical verb of a verb phrase and expresses grammatical distinctions not carried by the lexical verb, such as person, number, tense, mood, aspect, voice or evidentiality, 'CCONJ': A coordinating conjunction is a word that links words or larger constituents without syntactically subordinating one to the other and expresses a semantic relationship between them, 'DET': Determiners are words that modify nouns or noun phrases and express the reference of the noun phrase in context, 'INTJ': An interjection is a word that is used most often as an exclamation or part of an exclamation, 'NOUN': Nouns are a part of speech typically denoting a person, place, thing, animal or idea, 'NUM': A numeral is a word, functioning most typically as a determiner, adjective or pronoun, that expresses a number and a relation to the number, such as quantity, sequence, frequency or fraction, 'PART': Particles are function words that must be associated with another word or phrase to impart meaning and that do not satisfy definitions of other universal parts of speech, 'PRON': Pronouns are words that substitute for nouns or noun phrases, whose meaning is recoverable from the linguistic or extralinguistic context, 'PROPN': A proper noun is a noun (or nominal content word) that is the name (or part of the name) of a specific individual, place, or object, 'PUNCT': Punctuation marks are non-alphabetical characters and character groups used in many languages to delimit linguistic units in printed text, 'SCONJ': A subordinating conjunction is a conjunction that links constructions by making one of them a constituent of the other. The subordinating conjunction typically marks the incorporated constituent which has the status of a (subordinate) clause, 'SYM': A symbol is a word-like entity that differs from ordinary words by form, function or both, 'VERB': A verb is a member of the syntactic class of words that typically signal events and actions, can constitute a minimal predicate in a clause, and govern the number and types of other constituents which may occur in the clause, 'X': The tag X is used for words that for some reason cannot be assigned a real part-of-speech category.
Sentence: {{ where }} can I find the best tours to the Mekong Delta at reasonable prices ?
Word: where
|
ADV
| 5
|
NIv2
|
task583_udeps_eng_coarse_pos_tagging
|
zs_opt
|
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] |
In this task, you are given a sentence from the Bible in Persian, and your task is to translate it into English.
و هنگامی که ابر را بالای زمین گسترانم، و قوس در ابر ظاهرشود،
|
And it shall come to pass, when I bring a cloud over the earth, that the bow shall be seen in the cloud:
| 0
|
NIv2
|
task654_bible_fa_en_translation
|
zs_opt
|
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] |
In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
[Q]: A plastic tarp, stolen from the highway department, capped the works.
[A]: Singular
[Q]: These ideas already existed at that time from Hinduism.
[A]: Plural
[Q]: The bastard didn't just have his hands all over Mia, but his mouth was on her too.
[A]:
|
Singular
| 5
|
NIv2
|
task430_senteval_subject_count
|
fs_opt
|
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] |
Given the task definition and input, reply with output. Given a set of four words, generate the category that the words belong to. Words are separated by commas. The possible categories are social gathering, accomodation, physical property, measurement unit, corporate, nutritional value, boats, police punishment, location (proximity), card games, outdoor sport activity, military rank, baby animals, weather, consumer product, animals, boxing terminology, food, chocolate food, bird, type of sandwich, date status, body part, ocean, employment activity, moral characteristic, elements, poker, computers, construction, guitar part, shady activities, physical activity, kitchenware, temperature, type of rigidity, emotional status, season, mode of transportation, window material, activity, emotional display, geographical property, fried food, store status, widespread, aesthetic characteristic, alcoholic drinks, secretary duty, direction, personal characteristic, and animal.
prison, fine, jail time, custody
|
police punishment
| 5
|
NIv2
|
task143_odd-man-out_classification_generate_category
|
zs_opt
|
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] |
You are given a statement written in Hindi. Choose the most logical word from the given 4 options which can be used to replace the <MASK> token in the statement. Output the word from the correct option .
Example input: Statement: सिग्नेचर केल्विन क्लेन अंडरवियर बुटीक, ब्यूनस आयर्स, टोरंटो, मेक्सिको सिटी, <MASK>, ग्लासगो, मेलबोर्न, हांगकांग, लंदन, मैनचेस्टर, न्यूयॉर्क शहर, शंघाई, फ्रैंकफर्ट एम् मेन, सिंगापुर में देखे जा सकते हैं। केल्विन क्लेन अंडरवियर, कार्डिफ़ के सेंट डेविड शॉपिंग सेंटर में भी 2010 में क्रिसमस से पहले एक दुकान खोलने जा रहा है।
Option A: मैनचेस्टर
Option B: मैनचेस्टर
Option C: एडिनबर्ग
Option D: मेलबोर्न
Example output: एडिनबर्ग
Example explanation: The most suitable word from the given options to replace the <MASK> token is एडिनबर्ग as the rest places have already been stated in the statement .
Q: Statement: <MASK>, केरल, महाराष्ट्र और उड़ीसा के दक्षिण भारतीय ब्राह्मण इस पर्व को अवनि अवित्तम कहते हैं। यज्ञोपवीतधारी ब्राह्मणों के लिये यह दिन अत्यन्त महत्वपूर्ण है। इस दिन नदी या समुद्र के तट पर स्नान करने के बाद ऋषियों का तर्पण कर नया यज्ञोपवीत धारण किया जाता है। गये वर्ष के पुराने पापों को पुराने यज्ञोपवीत की भाँति त्याग देने और स्वच्छ नवीन यज्ञोपवीत की भाँति नया जीवन प्रारम्भ करने की प्रतिज्ञा ली जाती है। इस दिन यजुर्वेदीय ब्राह्मण 6 महीनों के लिये वेद का अध्ययन प्रारम्भ करते हैं।
Option A: उत्तरांचल
Option B: तमिलनाडु
Option C: बंगाल
Option D: उड़ीसा
A:
|
तमिलनाडु
| 3
|
NIv2
|
task947_wiki_cloze_hi_multiple_choice_question_answering
|
fs_opt
|
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] |
Combine the given two facts to write a concluding fact. Note that there should be some parts of the first and second facts that are not mentioned in this conclusion fact. Your combined fact should be the result of a chain between the two facts. Chains form when two facts connect together to produce a concluding fact. An example of a chain is: "pesticides cause pollution" (fact1) + "pollution can harm animals" (fact2) → "pesticides can harm animals" (conclusion fact). Parts of the concluding fact overlap with the first and the second fact. To construct such a concluding fact, a useful way is to borrow the subject from one fact and the ending conclusions from another fact.
Q: Fact 1: Cancer cells divide more often than normal cells, and grow out of control.
Fact 2: Cancer is an example of a mutation in cells.
A: Mutation can cause growth out of control.
****
Q: Fact 1: Behaviors that improve fitness increase through natural selection.
Fact 2: Many organism behaviors have evolved through natural selection.
A: An organism's fitness is a result of evolution.
****
Q: Fact 1: an earthquake causes the ground to shake.
Fact 2: Earthquakes are caused by fault movement.
A:
|
Fault movement causes the ground to shake.
****
| 4
|
NIv2
|
task038_qasc_combined_fact
|
fs_opt
|
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] |
You will be given a definition of a task first, then some input of the task.
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair neither agree nor contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Sentence 1: A black dog rolling in grass. Sentence 2: A dog plays outside.
Output:
|
A dog rolls in the grass, excited spring is finally here.
| 1
|
NIv2
|
task184_snli_entailment_to_neutral_text_modification
|
zs_opt
|
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] |
Detailed Instructions: In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ': Adjectives are words that typically modify nouns and specify their properties or attributes, 'ADP': Adposition is a cover term for prepositions and postpositions, 'ADV': Adverbs are words that typically modify verbs for such categories as time, place, direction or manner, 'AUX': An auxiliary is a function word that accompanies the lexical verb of a verb phrase and expresses grammatical distinctions not carried by the lexical verb, such as person, number, tense, mood, aspect, voice or evidentiality, 'CCONJ': A coordinating conjunction is a word that links words or larger constituents without syntactically subordinating one to the other and expresses a semantic relationship between them, 'DET': Determiners are words that modify nouns or noun phrases and express the reference of the noun phrase in context, 'INTJ': An interjection is a word that is used most often as an exclamation or part of an exclamation, 'NOUN': Nouns are a part of speech typically denoting a person, place, thing, animal or idea, 'NUM': A numeral is a word, functioning most typically as a determiner, adjective or pronoun, that expresses a number and a relation to the number, such as quantity, sequence, frequency or fraction, 'PART': Particles are function words that must be associated with another word or phrase to impart meaning and that do not satisfy definitions of other universal parts of speech, 'PRON': Pronouns are words that substitute for nouns or noun phrases, whose meaning is recoverable from the linguistic or extralinguistic context, 'PROPN': A proper noun is a noun (or nominal content word) that is the name (or part of the name) of a specific individual, place, or object, 'PUNCT': Punctuation marks are non-alphabetical characters and character groups used in many languages to delimit linguistic units in printed text, 'SCONJ': A subordinating conjunction is a conjunction that links constructions by making one of them a constituent of the other. The subordinating conjunction typically marks the incorporated constituent which has the status of a (subordinate) clause, 'SYM': A symbol is a word-like entity that differs from ordinary words by form, function or both, 'VERB': A verb is a member of the syntactic class of words that typically signal events and actions, can constitute a minimal predicate in a clause, and govern the number and types of other constituents which may occur in the clause, 'X': The tag X is used for words that for some reason cannot be assigned a real part-of-speech category.
Q: Sentence: Traveler s guide - For the {{ young }} and unknowledgeable ?
Word: young
A:
|
ADJ
| 9
|
NIv2
|
task583_udeps_eng_coarse_pos_tagging
|
zs_opt
|
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0.2385418713092804
] |
You are given a sentence in English. Your job is to translate the English sentence into Polish.
Let me give you an example: It's sort of the biggest TiVo box you've ever seen.
The answer to this example can be: To największa nagrywarka, jaką w życiu widzieliście.
Here is why: The English sentence is correctly translated into Polish, because the meaning is preserved.
OK. solve this:
Well, let's hold on to that thought for just a minute.
Answer:
|
Zatrzymajmy się chwilę przy tej myśli.
| 8
|
NIv2
|
task1092_ted_translation_en_pl
|
fs_opt
|
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] |
In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A preceding the last i elements. i will always have a value less than the length of A.
Q: 1, ['t', 'O', '5607', '1577', '9143', '5197', 'y', 'd', 'w', 's', '3331', '8701', '9409']
A:
|
t, O, 5607, 1577, 9143, 5197, y, d, w, s, 3331, 8701
| 4
|
NIv2
|
task078_all_elements_except_last_i
|
zs_opt
|
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You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
You are given a sentence in Portuguese. Your job is to translate the Portuguese sentence into English.
Os astrónomos acreditam que cada estrela da galáxia tem um planeta, e especulam que até um quinto deles tem um planeta do tipo da Terra que poderá ter vida, mas ainda não vimos nenhum deles.
Solution: Astronomers now believe that every star in the galaxy has a planet, and they speculate that up to one fifth of them have an Earth-like planet that might be able to harbor life, but we haven't seen any of them.
Why? The Portugese sentence is correctly translated into English, because the meaning is preserved.
New input: Não produz mais energia do que a que se põe dentro, mas mesmo assim faz umas coisas muito fixes.
Solution:
|
It doesn't produce more energy out than I put in, but it still does some pretty cool stuff.
| 0
|
NIv2
|
task1274_ted_translation_pt_en
|
fs_opt
|
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] |
Part 1. Definition
You are given a sentence in Japanese. Your job is to translate the Japanese sentence into Farsi.
Part 2. Example
私たちはただひたすら歌い続けましたすると驚くことに信頼が芽生え友情が花開いたのです
Answer: ما آواز خوندیم ، و خوندیم ، آواز خونديم ، و بطور شگفت انگیزی اعتماد جدید رشد کرد ، و درواقع دوستی شکوفه زد.
Explanation: The Japanese sentence is correctly translated into Farsi, because the meaning is preserved.
Part 3. Exercise
（ ピンカー ） 色々あるけれど共感する対象が広がったことだろう
Answer:
|
استیون: در واقع ، در بین دیگر چیزها ، دایره ی همدلیِ ما گسترش یافته است.
| 7
|
NIv2
|
task1098_ted_translation_ja_fa
|
fs_opt
|
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] |
You will be given a definition of a task first, then some input of the task.
A text is given in Telugu. Translate it from the Telugu language to the Urdu language. The translation must not omit or add information to the original sentence.
యుటి ఉద్యోగుల కు సెల్ఫ్- ఫైనాన్సింగ్ హౌసింగ్ స్కీము లో భాగం గా 3930 మంది అలాటీ లకు అపార్ట్ మెంట్ ల నిర్మాణం కోసం చండీగఢ్ హౌసింగ్ బోర్డు కు భూమి కేటాయింపు ప్రతిపాదన కు ఆమోదం తెలిపిన మంత్రివర్గం
Output:
|
مرکزکے زیر انتظام علاقو ں کے ملازمین کے لئے ازخود سرمایہ کاری ہاؤسنگ اسکیم کے تحت تین ہزار نوسو تیس (3930) الاٹیوں کے لئے اپارٹمنٹ کی تعمیر کی غرض سے چنڈی گڑھ ہاؤسنگ بورڈ کو اراضی الاٹ کرنے کی تجویز کو کابینہ کی منظوری
| 1
|
NIv2
|
task1037_pib_translation_telugu_urdu
|
zs_opt
|
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-0.22121378779411316,
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-0.7871413826942444,
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-0.6438161134719849,
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-1.1966021060943604,
-0.33678025007247925,
-0.9163196086883545
] |
Detailed Instructions: Given a sentence, an entity and its sentiment towards the entity, verify if it is the correct sentiment towards the entity. Answer should be yes or no. Note that URLs in the text have been replaced with [Link].
Problem:Verify if the sentiment of the following document towards the entity Cuomo is Neutral . The review board awarding the funds has approved 427 plans for projects covering 322 school districts and 20 special education schools Azzopardi said.
Solution:
|
yes
| 8
|
NIv2
|
task422_persent_sentence_sentiment_verification
|
zs_opt
|
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] |
Teacher: In this task you will be given a list, of lists, of integers. For every inner list contained in the input list, you should multiply every even number in that list. The output should be a list of integers with the same length as the number of lists in the input list. If there are no even numbers in an inner list you should output 0 for that list.
Teacher: Now, understand the problem? If you are still confused, see the following example:
[[7, -3, -3, 11], [-6, -6, -5, 2], [-8, 4, -3]]
Solution: [0, 72, -32]
Reason: The first inner list has no even integers, so the first number in the output is 0. The second list has -6, -6, 2 for even integers so the second output is 72. The third list has -8, 4 as even numbers so the third output is -32. This is a good example.
Now, solve this instance: [[-36, 9, 46], [29, -9], [2, 26, -5], [1, 23], [32, -39, 30, -31], [-18, -18]]
Student:
|
[-1656, 0, 52, 0, 960, 324]
| 2
|
NIv2
|
task851_synthetic_multiply_evens
|
fs_opt
|
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] |
Detailed Instructions: In this task, you are given a sentence in the English and Japanese language. Your task is check if the Japanese sentence is translation of English. if the translation is correct than generate label "Yes", otherwise generate label "No".
Q: English: The sculptures, a new replica of the "Goddess of Democracy" and a carving called the "Tiananmen Massacre Relief", had been placed in the piazza of Hong Kong's Times Square shopping mall in remembrance of the 21st anniversary of the Tiananmen Square Incident.
Japanese: その彫刻は、「民主主義の女神」の新しいレプリカと、「天安門事件のレリーフ」といわれる彫刻で、香港のショッピングモール、タイムズスクウェアの広場に、天安門事件の21周年の記念として設置されていた。
A:
|
Yes
| 9
|
NIv2
|
task437_alt_en_ja_answer_generation
|
zs_opt
|
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] |
Given a sentence in German, generate a new German sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
--------
Question: Sie sind wahrscheinlich, um einen Arm herum in Käse zu harken.
Answer: Sie werden wahrscheinlich ein Chaos in Käse zu erkennen.
Question: Ein Bauernkind erwacht oft in einem Bauernhaus auf dem Land, das sie pflegen.
Answer: Eine landwirtschaftliche Familie lebt oft in einem Bauernhaus auf dem Land, das sie schützen.
Question: Das Schachspiel wird auf einer Schachplatte gespielt.
Answer:
|
Das Schachspiel wird auf einem Schachstück gespielt.
| 7
|
NIv2
|
task416_mickey_de_sentence_perturbation_generation
|
fs_opt
|
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] |
In this task you are given a sentence with one coordinating conjunction (for, and, nor, but, or, yet, and so). You must judge whether the order of two coordinated clausal conjoints have been inverted or not. Inversion would result in the sentence sounding unnatural. Label the instances as "Original" or "Inversion" based on your judgment.
Let me give you an example: He knew it , and he deserved no answer .
The answer to this example can be: Inversion
Here is why: "He knew it" and "he deserved no answer" have been inverted.
OK. solve this:
There was no end and there was no light .
Answer:
|
Original
| 8
|
NIv2
|
task516_senteval_conjoints_inversion
|
fs_opt
|
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] |
Part 1. Definition
In this task, you are given books product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
Part 2. Example
少年心 . 重松さんの作品の中でも、一番共感できる作品。 周りの人達に心を閉ざし、気が付いたら周りと距離を置くようになっていた少年が徐々に成長し、心を開いていく様子がとても鮮明に表現されている。 人間誰でも他人に対して嫌悪感を抱いてしまったり・・・周りにどう思われるかを気にして、意地を張って素直になれなかったりする。
Answer: POS
Explanation: The overall sentiment of the review is positive as the reviewer refers to the book with positive expressions such as '一番共感できる作品'. Hence, the label is 'POS'.
Part 3. Exercise
深みがなかったです。 . 話題作だと言う事で読んでみましたが、内容の浅さにがっくりきました。 読書量の少ない人たちにとっては 本を読むという行為が そんなに難しいことではないと感じることができるので 功績はあるのかもしれませんが、 本物を求めている人たちにとっては 物足りない内容だと思います。 物語の展開の仕方も単調であり、文章が美しいわけでもなく、 テーマの掘り下げ方も実に浅く 何度も読み返して 人生とは何かと考えたくなるという部類の本ではないような印象を受けました。 職人の技が光る味でもなく 母親のぬくもりのある味でもなく インスタント食品の味のような本だなぁというのが 率直な感想でした。 人口甘味料のような人工的な味という言い方もできると思います。 歌い継がれる歌ではなく、ただ乗りだけで流行して すぐに忘れ去られる中身のない歌のような感じです。 川の流れのようにのような素晴らしい歌を作られた秋元氏の作品なのに非常に残念です。 同じお金を払うなら もっと中身のある本をお奨めします。
Answer:
|
NEG
| 7
|
NIv2
|
task485_cls_japanese_books_classification
|
fs_opt
|
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] |
In this task, you are given a public comment from online platforms. You are expected to classify the comment into two classes: insult and non-insult. Insult is any lanugage or act that is disrespectful or scornfully abusive.
One example is below.
Q: Comment: your posts shows you are stupid period
A: Insult
Rationale: This comment is insult because it is scornful and disrespectful.
Q: Comment: Women can be sexist pigs too. Take it from a woman...............the author of this article IS a sexist lying pig. For some reason she thinks she can practice what she preaches against as long as its white males she's talking about . And then she thinks that she can say it out loud without it being found out how disgusting and hateful she is. She's kind of stupid too.
A:
|
Insult
| 9
|
NIv2
|
task328_jigsaw_classification_insult
|
fs_opt
|
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] |
Given an input stream, the objective of this task is to classify whether words in the stream are grammatically correct or not. The input to this task is a stream of words, possibly from captions generated by a speech-to-text engine, and the output is a classification of each word from the labels (reason) = [NO_DIFF (correct), CASE_DIFF (case error), PUNCUATION_DIFF (punctuation error), CASE_AND_PUNCUATION_DIFF (both case and punctuation error), STEM_BASED_DIFF (stem word error), DIGIT_DIFF (digit error), INTRAWORD_PUNC_DIFF (intra-word punctuation error), and UNKNOWN_TYPE_DIFF (an error that does not corrrespond to the previous categories)].
['what', "we're", 'going', 'to', 'do', 'in', 'this', 'video', 'is', 'talk', 'about', 'the', 'various', 'types', 'of', 'discontinuities', 'that', "you've", 'probably', 'seen', 'when', 'you', 'took', 'algebra', 'or', 'precalculus', 'but', 'then', 'relate', 'it', 'to', 'our', 'understanding', 'of', 'both', 'two', 'sided', 'limits', 'and', 'one-sided', 'limits', 'so', "let's", 'first', 'review', 'the', 'classification', 'of', 'discontinuities', 'so', 'here', 'on', 'the', 'left', 'you', 'see', 'that', 'this', 'curve', 'looks', 'just', 'like', 'y', 'equals', 'x', 'squared', 'until', 'we', 'get', 'to', 'x', 'equals', '3', 'and', 'instead', 'of', 'it', 'being', '3', 'squared', 'it', 'does', 'that', 'at', 'this', 'point', 'you', 'have', 'this', 'opening', 'and', 'instead', 'the', 'function', 'at', '3', 'is', 'defined', 'at', '4']
['NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'DIGIT_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'DIGIT_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'DIGIT_DIFF']
['hello', 'readers', 'today', 'we', 'shall', 'take', 'the', 'field', 'in', 'rhetorical', 'combat', 'also', 'known', 'as', 'argument', 'argument', 'is', 'when', 'an', 'author', 'wants', 'to', 'convince', 'you', 'of', 'their', 'position', 'this', 'is', 'my', 'position', 'you', 'should', 'share', 'this', 'position', 'and', 'here', 'is', 'why', 'argument', 'does', 'not', 'sneak', 'it', 'does', 'not', 'come', 'in', 'through', 'the', 'back', 'it', 'is', 'supported', 'by', 'reasons', 'evidence', 'examples', 'not', 'just', 'pure', 'opinion', 'not', 'just', 'cherry-picked', 'stories', 'data', 'fact', 'observable', 'reality', 'there', 'is', 'a', 'place', 'for', 'pure', 'subjective', 'opinion', 'and', '', 'is', 'the', 'realm', 'of', 'the', 'movie', 'and', 'the', 'restaurant', 'review', 'I', 'did', 'not', 'like', 'this', 'movie', 'because', 'I', "didn't"]
['NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_AND_PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'PUNCUATION_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'PUNCUATION_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF']
['[Music]', 'hi', 'so', "I'd", 'like', 'to', 'talk', 'a', 'little', 'bit', 'about', 'the', 'people', 'who', 'make', '', 'things', 'we', 'use', 'every', 'day', 'our', 'shoes', 'our', 'handbags', 'our', 'computers', 'and', 'cell', 'phones', 'now', 'this', 'is', 'a', 'conversation', 'that', 'often', 'calls', 'up', 'a', 'lot', 'of', 'gilts', 'imagine', 'the', 'teenage', 'farm', 'girl', 'who', 'makes', 'less', 'than', '$1', 'an', 'hour', 'stitching', 'your', 'running', 'shoes', 'where', 'the', 'young', 'Chinese', 'man', 'who', 'jumps', 'off', 'a', 'rooftop', 'after', 'working', 'overtime', 'assembling', 'your', 'iPad', 'we', 'the', 'beneficiaries', 'of', 'globalization', 'seem', 'to', 'exploit', 'these', 'victims', 'with', 'every', 'purchase', 'we', 'make', 'and', 'the', 'injustice', 'feels', 'embedded', 'in', 'these', 'products', 'themselves', 'after']
|
['NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_AND_PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'UNKNOWN_TYPE_DIFF', 'CASE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'UNKNOWN_TYPE_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_AND_PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'NO_DIFF', 'UNKNOWN_TYPE_DIFF', 'NO_DIFF', 'PUNCUATION_DIFF', 'CASE_DIFF']
| 0
|
NIv2
|
task1416_youtube_caption_corrections_incorrect_grammar_classification
|
fs_opt
|
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In this task you will be given a text passage from a trivia quiz. You should choose the category this question belongs to from these categories: History, Science, Social Science, Fine Arts, Literature. You can only choose one category, so if in doubt, please choose the closest match.
Let me give you an example: In one work this author details the life of a tomb-maker who makes extra money by playing the organ at an insane asylum, while another of his novels sees Swartz give a refugee proper documentation only after he tells him his life story in the title Portuguese city. In addition to The Black Obelisk and The Night in Lisbon, he also wrote about Robert and Otto selling their auto workshop and visiting Patrice in a Swiss tuberculosis sanatorium in his novel The Three Comrades, while the turbulent life of the German surgeon Ravic is told in The Arch of Triumph. Another of his novels, The Road Back, is thought to be a sequel to his most famous book, in which the patriotic teacher Kantorek convinces Paul Bumer and his friends to join the Germany Army during World War I. For 10 points, name this German author who wrote All Quiet on the Western Front.
The answer to this example can be: Literature
Here is why: The text is talking about an author and their works, so it belongs to theliterature category.
OK. solve this:
military defeat for this country inspired a play also based on the Oedipus myth, You Killed the Monster; that same author from this country also wrote the short story collections City of Love and Ashes and The Cheapest Nights and Other Stories. One novel set in this country sees the protagonist cheat on his wife Kamelia with the dancer Warda and the singer Margarete. Besides The Beggar, a work sometimes considered this country's first novel sees the title peasant girl die of TB after her lover is conscripted; that novel is titled Zaynab. A Nobel laureate from this country described a man who had children with names like Jesus, Moses and Muhammad in (*) Children of Gebelawi. That same author from this country was a co-patriot of Yusuf Idris and penned works like Sugar Street and Palace Walk. For 10 points, name this home to Naguib Mahfouz, author of the Cairo Trilogy.
Answer:
|
Literature
| 8
|
NIv2
|
task521_trivia_question_classification
|
fs_opt
|
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] |
In this task, you will be presented with a question and you have to answer the question based on your knowledge. Your answers should be as short as possible.
Example input: Who were the builders of the mosque in Herat with fire temples ?
Example output: Ghurids
Example explanation: This is a good example. Ghurids were the builders of the mosque in Herat with fire temples.
Q: What is the position of the player whose school 's colors are purple and white ?
A:
|
First baseman
| 3
|
NIv2
|
task344_hybridqa_answer_generation
|
fs_opt
|
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] |
You're given a sentence and your task is to classify whether the sentence is acceptable or not. Any sentence which is grammatically correct, has a naturalistic text, is written by a native speaker and which minimizes superfluous content is acceptable, otherwise unacceptable. If the sentence is acceptable then write "acceptable", otherwise "unacceptable".
[EX Q]: Handsome though everyone expects me to try to force Bill to make Mom agree that Dick is, I'm still going to Marry Herman.
[EX A]: acceptable
[EX Q]: John talked to a woman.
[EX A]: acceptable
[EX Q]: Is likely Jean to leave.
[EX A]:
|
unacceptable
| 6
|
NIv2
|
task616_cola_classification
|
fs_opt
|
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] |
Teacher:In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
Teacher: Now, understand the problem? Solve this instance: از کدامیک از برنامه های کاربردی زیر برای انجام امور حسابرسی و مالی استفاده می شود؟
Student:
|
common_knowledge
| 6
|
NIv2
|
task474_parsinlu_mc_classification
|
zs_opt
|
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] |
A text is given in Tamil. Translate it from the Tamil language to the Urdu language. The translation must not omit or add information to the original sentence.
Example Input: மூன்றாம் மற்றும் நான்காம் இடத்திற்குப் பாராட்டுச் சான்றிதழ் வழங்கப்படுகிறது.
Example Output: تیسری اور چوتھی پوزیشن پانے والے پیپرس کے لئے سرٹیفکٹ اور توصیف نامے پیش کئے گئے۔
Example Input: 5 ஏக்கர் நிலம் சண்டிகார் வீட்டுவசதி வாரியத்துக்கு ஒதுக்கப்படும்.
Example Output: 5 ایکڑ زمیں جو سرکاری تحویل میں ہے ،اسے چنڈی گڑھ ہاؤسنگ بورڈ کو الاٹ کیا جانا ہے ۔
Example Input: 6 சதவீதம் என்ற சராசரி பணவீக்கம், தாராளமயமாக்கல் நடவடிக்கையை இந்தியா தொடங்கிய 1991-ம் ஆண்டு முதல் எந்தவொரு இந்திய அரசிலும் இல்லாத குறைந்தபட்ச விகிதமாகும்,” என்றார்.
Example Output:
|
6فیصد کی شرح افراط زر 1991سے کسی بھی بھارتی حکومت کے لئے سب سے کم ہے۔ جب سے حکومت نے لبرلائزیشن(نرم کاری) کا عمل شروع کیاتھا’’۔
| 3
|
NIv2
|
task1035_pib_translation_tamil_urdu
|
fs_opt
|
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] |
Instructions: You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Japanese.
Input: قيل لهم بأن هذه الجزر و الكثير من الجزر المحيطة تسكنها أكلة لحوم البشر
Output:
|
マルキーズ諸島とその周辺には食人種が住んでいるという噂です
| 3
|
NIv2
|
task1231_ted_translation_ar_ja
|
zs_opt
|
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] |
In this task, you're given a short article. Your job is to classify the article based on its category. Use the following classification labels, 0. World, 1. Sports, 2. Business, 3. Science or Technical. Label the text "0" if it contains information related to world. Label the text "1" if it contains information related to sports. Label the text "2" if it contains information related business. Label the text "3" if it contains science or technical related information.
Ratings Lowest Ever for Open Men's Final (AP) AP - Roger Federer's dominant win over Lleyton Hewitt was the lowest-rated U.S. Open men's final ever.
|
1
| 0
|
NIv2
|
task1541_agnews_classification
|
zs_opt
|
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] |
Given a sentence in Dutch, generate a new Dutch sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
Example Input: U bent leuk om een bank te vinden in een financieel centrum.
Example Output: U zult waarschijnlijk een bank vinden in een financiële instelling.
Example Input: Mensen dragen patronen om te voorkomen dat schommelen hun voeten.
Example Output: Mensen dragen voeten om te voorkomen dat ze hun voeten overspoelen.
Example Input: Je kunt een spiegel gebruiken om je identiteit te zien.
Example Output:
|
U kunt een klik gebruiken om uw reflectie te zien.
| 3
|
NIv2
|
task409_mickey_nl_sentence_perturbation_generation
|
fs_opt
|
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] |
Given the task definition and input, reply with output. Given a sentence in Somali language, translate the sentence to English language keeping the meaning of the original sentence intact
Somali sentence: Ilaah waxaynu u caabudaynaa si waafaqsan fahankeenna.
|
We do worship God according to what our understanding is of him.
| 5
|
NIv2
|
task450_opus_paracrawl_so_en_translation
|
zs_opt
|
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Detailed Instructions: In this task, you're expected to write answers to questions involving multiple references to the same entity. The answer to the question should be unambiguous and a phrase in the paragraph. Most questions can have only one correct answer.
Q: Passage: In an interview with Pitchfork in October 2007, Taylor said there would be an equal proportion of electronic elements to live material, as the band doesn't "do things by adding one thing and taking something else away". The album contained maximalist and minimalist songs; several tracks on the album were influenced by rock and heavy metal music, and the track "Wrestlers" started taking a new direction because the band was "wrestling with the idea of making an R. Kelly kind of slick R and B number" and ultimately "[sounded] more like Randy Newman's "Short People". He said, "if the press release says it's faster and rockier it doesn't account for the fact that there are more ballads on this record than any other record." Taylor said that feelings of happiness and love influenced the album's romantic feel.Goddard considered varying styles and influences a key factor in the band's music. He explained to The Sun that creating music could be difficult because a member could introduce a different influence. Goddard and Doyle said that clashes and restlessness during recording led to "unpleasant" periods of silence, but ultimately thought the clashes created "something more interesting because you have these different voices and not one person dictating".Martin told The Georgia Straight that the group are "afflicted with something akin to musical attention-deficit disorder" and said that the group "get bored quite easily [...] with [their] own records at times". He elaborated by saying that the group aren't "really interested in reproducing the same sound" because they don't find it exciting.Taylor stated Hot Chip "didn't set out to make something with one mood" and that he thought the band's style of "jump[ing] all over the place stylistically" made sense as a record. In an interview with The Georgia Straight, Martin expressed that Hot Chip didn't want to create a "'classic' record that would have a particular sound" as they wanted to make music that was "quite experimental and out-there". Made in the Dark was intended to represent the "whole live sound of the band" and they are "a band as much as originally having been a duo".
Question: What was the name of the album that was not a "classic" record?
A:
|
Made in the Dark.
| 9
|
NIv2
|
task002_quoref_answer_generation
|
zs_opt
|
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] |
Detailed Instructions: You are given a sentence in Polish. Your job is to translate the Polish sentence into Hebrew.
Q: (śmiech) Ten kontyngent wydawał się mieć szczęście i nie odniósł żadnych strat, pomimo tego, że wielu żołnierzy sił pokojowych zostało zabitych w Liberii.
A:
|
(צחוק) נציגות זו זכתה למזל גדול, ולא ספגה אבדות כלל, אף על פי שעשרות מכוחות השלום נהרגו בליבריה.
| 9
|
NIv2
|
task1260_ted_translation_pl_he
|
zs_opt
|
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] |
TASK DEFINITION: In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only: returns whether there is exactly one row in the view
3. hop: returns the value under the header column of the row
4. and: returns the boolean operation result of two arguments
5. max/min/avg/sum: returns the max/min/average/sum of the values under the header column
6. nth max/nth min: returns the n-th max/n-th min of the values under the header column
7. argmax/argmin: returns the row with the max/min value in header column
8. nth argmax/nth argmin: returns the row with the n-th max/min value in header column
9. eq/not eq: returns if the two arguments are equal
10. round eq: returns if the two arguments are roughly equal under certain tolerance
11. greater/less: returns if argument 1 is greater/less than argument 2
12. diff: returns the difference between two arguments
13. filter eq/not eq: returns the subview whose values under the header column is equal/not equal to argument 3
14. filter greater/less: returns the subview whose values under the header column is greater/less than argument 3
15. filter greater eq /less eq: returns the subview whose values under the header column is greater/less or equal than argument 3
16. filter all: returns the view itself for the case of describing the whole table
17. all eq/not eq: returns whether all the values under the header column are equal/not equal to argument 3
18. all greater/less: returns whether all the values under the header column are greater/less than argument 3
19. all greater eq/less eq: returns whether all the values under the header column are greater/less or equal to argument 3
20. most eq/not eq: returns whether most of the values under the header column are equal/not equal to argument 3
21. most greater/less: returns whether most of the values under the header column are greater/less than argument 3
22. most greater eq/less eq: returns whether most of the values under the header column are greater/less or equal to argument 3
PROBLEM: eq { count { filter_eq { all_rows ; city of license ; san antonio } } ; 4 } = true
SOLUTION: select the rows whose city of license record fuzzily matches to san antonio . the number of such rows is 4 .
PROBLEM: most_eq { all_rows ; details ; abandoned part of northern heights project } = true
SOLUTION: for the details records of all rows , most of them fuzzily match to abandoned part of northern heights project .
PROBLEM: most_eq { all_rows ; position ; guard } = true
SOLUTION:
|
for the position records of all rows , most of them fuzzily match to guard .
| 8
|
NIv2
|
task110_logic2text_sentence_generation
|
fs_opt
|
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instruction:
In this task, you are given a Reddit post as a text. Your task is to generate a short summary for this text. The summary must include a situation which caused humor. The summary should be one or two sentences long.
question:
Text: this happened a couple of nights ago, but lets just pretend it just happened today for the sake of r/tifu.
anyways, about a month ago i had a frenulectomy preformed on me by my urologist, on the recovery paper work it stated in big bold letters" no intercourse for 30 days". well it had been 30 days but i still felt a bit of tightness around the area where my foreskin is. so i had been talking to this girl that i hooked up with before and she was telling me how she was back in town and she wanted to see me. i haven't been able to touch my self for 30 days now, i was ready to explode so when she said she wanted to see me, i knew what she wanted and i was not going to refuse.
i invite her over and one thing leads to another and we are both naked on top of each other, now at this point she is going down on me and just rocking my world with her mouth, it was magical. after she was done playing with me, she starts to make her way back up to my mouth and we start making out again, but this time she is dry humping the crap out of my dick with her ass. she starts thrusting back and forth faster and harder with every cycle, i'm having a blast, enjoying the all the teasing and mind fucking she is giving me with her eyes and ass, until i feel something rip and a massive amount of pain over takes my fully erect penis. i have never thrown a girl of me so fast and hard in my life. fearfully i looked down at my shaft and all i see is a throbbing bloody dick, i bolt it to the bath tub and turn on the water. i quickly put my member under warm water but the blood wont stop coming out. after all, what is a hard on if not full of blood. after about 5 minutes, my erection is gone and the bleeding stoped. i inspect my self carefully and see a hole where the stitches use to be.
she comes into the bathroom and this tub looks like a murder scene, there is so much blood. she freaked out, asked me what happened, i explain and she tells me how sorry she was, got dress and left. still have not heard from her :(
answer:
i had a frenulectomy on my dick, tried having sex after a month, my foreskin ripped and the girl freaked out after seeing all the blood, still have not heard back from her
question:
Text: hi reddit, this did not happen today but i just felt like sharing my story.
i work in tech support, you know taking calls, creating tickets, resolving issues. at this one company, i got promoted to a second level position: no more calls, just a few tickets assigned to my queue, better pay, less hours. i was made. then came the fatidic day when the company fucked up and did not pay us on time, i was super mad, calling them and going to their office (they are a subcontractor, so we work on the clients office) and demanding an explanation, this was on a friday so i had to spend the weekend with no money; on monday they explain everything and promise to pay by friday and they did.
two weeks later was the companies christmas party and i was there with some friends, alcohol was consumed, jokes were made, then i got black out drunk and on my stupor i found the guy that explained why they did not paid on time and started trying to start a fight with him. a lot of people tried to break it up, and one of them was (i did not know then) the companies ceo. i pushed him away and told him "fuck off man, i will also kick your ass". they had to call the police to remove me from the premises.
on monday i was told who that one mas was. i tried to appeal to their good hearth and obviously they had to fire me. had to go back to taking calls and working as a slave on tech support.
answer:
got drunk on a company christmas party and tried to fight the ceo. then i was fired.
question:
Text: so this happened two days ago. i work at a supermarket chain, as a cashier. been there for about four years. pretty mindless job, the pay sucks, but it is a job noneless.
so anyway, there are a lot of younger girls (early 20's) that work at the same place, doing the same job. we don't count out our drawers, or have key locks on them - for some reason management thinks this is a good idea. we also have two video cameras in the entire store, one in the backroom, and one facing the parking lot. that is it
so on friday, one of the newer girls, who must have been working for about 3 months or so, comes barreling out of the managers office in the evening, her face beat red and balling her eyes out. apparently her register had come up short for the day (by 195 dollars), and my boss was threatening her with firing and arrest.
now i really don't know this girl very well, other then she is a single mom, trying to make ends meet, seems pretty normal enough.
this is where the fuck up happens. for some reason, i'm not sure what came over me, i stop in the path of my boss, who was in the process of following her out of the door, and start completely throwing myself under the bus. i told him i was on her register all day, and that i had told her to go check product dates instead. after giving me an incredulous look, and unleashing a barrage of yelling at me, he basically told me to get lost and don't come back, and expect to be hearing from the police. none of this actually registered until i left the store, what had happened.
so now i'm out of a job, so low paying that i don't even have a savings, and will probably be booted out of my apartment, and expecting to be charged. all for a girl that, for all i know, skimmed money out of the register.
answer:
|
took the blame for a girl at work that had a short register, backfired and got fired/possibly charged with theft
| 9
|
NIv2
|
task511_reddit_tifu_long_text_summarization
|
fs_opt
|
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] |
Teacher:A text is given in Hindi. Translate it from the Hindi language to the Marathi language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? Solve this instance: तो ये एक वो ही प्रयास है कि समाज में ऐसी जितनी भी शक्तियां हैं, उनको कैसे जोड़ा जाए?
Student:
|
तर हा एक असा प्रयत्न आहे ज्यात समाजातील जितक्या शक्ती आहेत त्यांना एकत्र कशा प्रकारे आणता येईल?
| 6
|
NIv2
|
task1081_pib_translation_hindi_marathi
|
zs_opt
|
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] |
instruction:
In this task you are given a tweet and you must identify whether the tweet contains any offense or any form of (untargeted) profanity. Label the post as NOT if the post does not contain offense or profanity. Non-offensive posts do not include any form of offense or profanity. label the post as OFFENSIVE if the post contains offensive language or a targeted (veiled or direct) offense. Twitter user mentions were substituted by @USER and URLs have been substitute by URL.
question:
@USER @USER Issss nigga fall asleep on soft after this
answer:
NOT
question:
@USER Wow was he wearing his hood when he made that statement? And... antifa? Really?
answer:
NOT
question:
@USER you are some basic white girl who clearly didn’t know any of them personally and it’s awful what has happened to Mac so why don’t you pay respect to that instead of being disgusting and blaming another innocent human in something that has nothing to do with them.
answer:
|
OFFENSIVE
| 9
|
NIv2
|
task286_olid_offense_judgment
|
fs_opt
|
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] |
You will be given a definition of a task first, then some input of the task.
You are given a conversation between two people. 'Person1:' and 'Person2:' are used to separate their respective dialogues. If the conversation begins with a question, label it '1' otherwise '0'.
Person1: Ladies and gentlemen , welcome to tonight's fashion show .
Person2: We see you are all dressed to the nines .
Person1: How very appropriate , because tonight we have a most dazzling show for you !
Person2: Yes , this runway is going to sparkle with glamour and style !
Person1: Some of tonight's highlights are dressed in the latest straight from Shanghai .
Person2: And others directly from New York .
Person1: So , sit back and relax and get your order forms ready .
Person2: Because you're going to love this show !
Output:
|
0
| 1
|
NIv2
|
task1534_daily_dialog_question_classification
|
zs_opt
|
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You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you will be given a short story. One sentence from the story is chosen. Consider the events that happen after that sentence. Is any of them directly caused by it, or is made possible by it? You should write your answer in the form " A >causes/enables> B". Try to use phrases and sentences from the story to compose your answer when possible. Do not change the main selected sentence in your answer.
story: Greg picked up his friend. They went off road trucking. The truck got stuck. Greg couldn't get it out himself. Greg got it out with his friend'.
selected sentence: Greg picked up his friend.
Solution: Greg picks up his friend >Causes/Enables> They go off road trucking
Why? The person picking up his friends makes them going off road trucking possible, so this is a correct answer.
New input: story: Garrett got a flat tire. He pulled over. People asked him to help. He told them to keep driving. He fixed the tire himself.
selected sentence: Garrett got a flat tire.
Solution:
|
Garrett gets a flat tire >Causes/Enables> He pulls over
| 0
|
NIv2
|
task748_glucose_reverse_cause_event_detection
|
fs_opt
|
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] |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Sentence 1: Jon saw his friend Tom coming out of the grocery store with a bag of fruit. Sentence 2: Tom had been shopping for fruit to give Jon.
Solution: Tom had gone into the store before he was seen by Jon.
Why? Tom must have gone into the store to come out of it.
New input: Sentence 1: A smiling girl, wearing a red top, sits in front of her fruit plate. Sentence 2: A grinning girl wearing a red dress, eats her fresh fruit while watching tv
Solution:
|
A grinning girl, sporting a red blouse, eats her strawberries in her chair
| 0
|
NIv2
|
task188_snli_neutral_to_entailment_text_modification
|
fs_opt
|
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] |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
This task is about translating a given English language sentence to Spanish.
It's this amazing wonderful feeling and you know it when you get it.
Solution: Es ese sentimiento asombroso e increíble, y lo reconoces cuando lo sientes.
Why? This is a good example because the English sentence has been translated correctly to Spanish.
New input: And this booking is within all the parameters for each individual.
Solution:
|
Y esta reserva cumple con todos los parámetros de cada individuo.
| 0
|
NIv2
|
task1691_qed_amara_translation
|
fs_opt
|
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] |
Indicate if the following Polish tweet contains cyber-bullying content with 'Yes'; otherwise, respond with 'No'.
--------
Question: Tweet: RT @anonymized_account JA JESTEM NORMALNY JA CHCE TYLKO ZAPOZOWAĆ Z ROWEREM PANI SYNA #topmodel , Question: Is the tweet free of any cyberbullying (harmful) content?
Answer: Yes
Question: Tweet: @anonymized_account A co robisz przy porannej kawie? Pasowanie? , Question: Does the tweet contain cyberbullying (harmful) content?
Answer: No
Question: Tweet: @anonymized_account I jak jest, bo już bym powoli wracała. , Question: Does the tweet contain cyberbullying (harmful) content?
Answer:
|
No
| 7
|
NIv2
|
task839_cdt_classification
|
fs_opt
|
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instruction:
You are given a paragraph (Passage), a question (Question) and two answer options (Option1 and Option2). Your task is to choose the correct answer to the question based on the passage. Answer of the question cannot be found directly from the passage, it is indirectly indicated in the passage. Do not answer with words other than the two options, and do not answer with option titles(e.g. "option1" or "1" are not acceptable answers.)
question:
Passage: My gym is about 3 miles from my house , so I shower at home after working out . Therefore , I put on my workout clothes at home . I have special shoes that I wear to support my feet well while working out . I drive the short trip to the gym . I do n't walk because it is often hotter than 110 degrees in Phoenix . At the gym , they scan my membership card so they know that I am a paying member . I take my gym bag to the locker room and leave my billfold and car keys in the locker while removing my exercise planning book and my weight lifting gloves from the gym left in the locker . I lift heavy weights to sustain my muscle mass . Then I use a stair machine to tone tone my legs and to build my respiratory capacity . Then I use the treadmill and walk 2.5 miles at a good pace for both my respiratory and circulatory systems . Question: What did they want to keep from hyper-extending? Option1: Their muscles. Option2: legs
answer:
Their muscles.
question:
Passage: I decided to go shopping for clothes , specifically , casual shirts . I 'm not a big fan of clothes shopping , because it 's not usually as easy as seeing something and just paying for it . I found the area where the shirts were that would be my size . Unfortunately it was being used . I shopped around for a little while longer until the dressing room was empty . I went into the room and took off the shirt I was wearing so I could try on the three shirts I had brought in . I knew there were other people wanting to use the dressing room , and I always get stressed when I know other people are in line behind me and waiting for me to finish . The three shirts fit , and I took them to the cashier to pay and get out of there . Question: What would they do if the shirts did not fit right? Option1: pay for the shirts Option2: Put the shirts back
answer:
Put the shirts back
question:
Passage: Today I organized a board game evening . I have a lot of friends who like to play board games , so it was pretty easy . First I had to call my 5 friends , and then I had to confirm their invitations . When the night of the evening came about , I put out different board games for us to play as well as different kinds of snacks . The first part of the night was just mingling and catching up with each other , which was a lot of fun . We ate different snacks such as chips and nuts as we talked about what games we wanted to play . I made sure to tell everyone the rules of the game , and we had a really great game night ! Question: What was set up promptly? Option1: board games Option2: The snack they wanted to eat
answer:
|
board games
| 9
|
NIv2
|
task165_mcscript_question_answering_commonsense
|
fs_opt
|
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] |
Detailed Instructions: In this task you will be given a list of numbers and you need to find the mean (average) of that list. The mean of a list can be found by summing every number in the list then dividing the result by the size of that list. The output should be rounded to 3 decimal places.
See one example below:
Problem: [1,3,5]
Solution: 3.000
Explanation: The mean of the input list is (1+3+5)/3, which equals 3. This is a good example.
Problem: [-68.707, 212.467, 153.116, 81.234, -62.635, -89.076, 58.18, -54.684]
Solution:
|
28.737
| 4
|
NIv2
|
task094_conala_calculate_mean
|
fs_opt
|
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