Update README.md

README.md

@@ -7,4 +7,26 @@ tags:
 pretty_name: MTEonLowResourceLanguage
 size_categories:
 - 1K<n<10K
----
+---
+Bengali is a low resource language in natural language processing (NLP), with dialects like Sylheti, Chittagong, and Barisal
+being even more underrepresented. To address this, ONUBAD introduced a parallel corpus translating these dialects into
+Standard Bangla and English using expert translators, providing 1,540 words, 130 clauses, and 980 sentences per dialect. 
+We focused on the Sylheti-English pair and adapted the dataset for LLM-based machine translation (MT) evaluation. 
+We extracted the 980 Sylheti-English sentence pairs, corrected inconsistencies, and added 520 new sentence pairs, 
+all translated by native speakers and cross-validated for accuracy, resulting in 1,500 high-quality pairs. To simulate a real-world
+MT evaluation scenario, we generated translations using the NLLB-200 model, recognized for its multilingual capabilities. 
+Two native Sylheti speakers evaluated the outputs using Direct Assessment (DA) guidelines, scoring based on semantic equivalence and fluency. 
+Scores were averaged and z normalized to reduce inter annotator variability and outliers.
+
+Our study that uses this dataset got accepted in CLNLP 2025. The [paper](https://arxiv.org/pdf/2505.12273) and [code](https://github.com/180041123-Atiq/MTEonLowResourceLanguage/tree/main) is attached for any technical reference.
+
+## Citation
+If you find our dataset or code useful in your research, please cite our paper:
+```
+@article{rahman2025llm,
+  title={LLM-Based Evaluation of Low-Resource Machine Translation: A Reference-less Dialect Guided Approach with a Refined Sylheti-English Benchmark},
+  author={Rahman, Md Atiqur and Islam, Sabrina and Omi, Mushfiqul Haque},
+  journal={arXiv preprint arXiv:2505.12273},
+  year={2025}
+}
+```