README.md

---
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}
}
```