| # Dataset Card for Taskmaster-1 | |
| - **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020 | |
| - **Paper:** https://arxiv.org/pdf/1909.05358.pdf | |
| - **Leaderboard:** None | |
| - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) | |
| ### Dataset Summary | |
| The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs, as seen for example in the restaurants, flights, hotels, and movies verticals. The music browsing and sports conversations are almost exclusively search- and recommendation-based. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface. | |
| - **How to get the transformed data from original data:** | |
| - Download [master.zip](https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip). | |
| - Run `python preprocess.py` in the current directory. | |
| - **Main changes of the transformation:** | |
| - Remove dialogs that are empty or only contain one speaker. | |
| - Split each domain dialogs into train/validation/test randomly (8:1:1). | |
| - Merge continuous turns by the same speaker (ignore repeated turns). | |
| - Annotate `dialogue acts` according to the original segment annotations. Add `intent` annotation (`==inform`). The type of `dialogue act` is set to `non-categorical` if the `slot` is not in `anno2slot` in `preprocess.py`). Otherwise, the type is set to `binary` (and the `value` is empty). If there are multiple spans overlapping, we only keep the shortest one, since we found that this simple strategy can reduce the noise in annotation. | |
| - Add `domain`, `intent`, and `slot` descriptions. | |
| - Add `state` by accumulate `non-categorical dialogue acts` in the order that they appear. | |
| - Keep the first annotation since each conversation was annotated by two workers. | |
| - **Annotations:** | |
| - dialogue acts, state. | |
| ### Supported Tasks and Leaderboards | |
| NLU, DST, Policy, NLG | |
| ### Languages | |
| English | |
| ### Data Splits | |
| | split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) | | |
| |------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------| | |
| | train | 13838 | 234321 | 16.93 | 9.1 | 1 | - | - | - | 100 | | |
| | validation | 1731 | 29349 | 16.95 | 9.15 | 1 | - | - | - | 100 | | |
| | test | 1734 | 29447 | 16.98 | 9.07 | 1 | - | - | - | 100 | | |
| | all | 17303 | 293117 | 16.94 | 9.1 | 1 | - | - | - | 100 | | |
| 7 domains: ['flights', 'food-ordering', 'hotels', 'movies', 'music', 'restaurant-search', 'sports'] | |
| - **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage. | |
| - **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage. | |
| ### Citation | |
| ``` | |
| @inproceedings{byrne-etal-2019-taskmaster, | |
| title = {Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset}, | |
| author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, | |
| booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}, | |
| address = {Hong Kong}, | |
| year = {2019} | |
| } | |
| ``` | |
| ### Licensing Information | |
| [**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |