| --- |
| license: cc-by-4.0 |
| task_categories: |
| - translation |
| tags: |
| - code |
| 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} |
| } |
| ``` |
