# Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language

arXiv: https://arxiv.org/abs/2311.11142 · HTML: https://mlsanity.com/paper/2311.11142/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla](https://mlsanity.com/paper/2205.11081/) (2022)
- [Unsupervised Neural Machine Translation with Weight Sharing](https://mlsanity.com/paper/1804.09057/) (2018)
- [A Survey on Document-level Neural Machine Translation: Methods and Evaluation](https://mlsanity.com/paper/1912.08494/) (2019)
- [English to Bangla Machine Translation Using Recurrent Neural Network](https://mlsanity.com/paper/2106.07225/) (2021)

## Built on by (most cited first, 10 of 10)

- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models](https://mlsanity.com/paper/2501.05749/) (2025)
- [ANUBHUTI: A Comprehensive Corpus For Sentiment Analysis In Bangla Regional Languages](https://mlsanity.com/paper/2506.21686/) (2025)
- [BIDWESH: A Bangla Regional Based Hate Speech Detection Dataset](https://mlsanity.com/paper/2507.16183/) (2025)
- [Transcribing Bengali Text with Regional Dialects to IPA using District Guided Tokens](https://mlsanity.com/paper/2403.17407/) (2024)
- [BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization](https://mlsanity.com/paper/2411.10879/) (2024)
- [KrishokChat: A Provenance-Traceable Multi-Task Bengali Agricultural Benchmark with Safety-Critical Chemical Advisory](https://mlsanity.com/paper/2606.29243/) (2026)
- [LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti](https://mlsanity.com/paper/2511.21761/) (2025)
- [Benchmarking Bengali Dialectal Bias: A Multi-Stage Framework Integrating RAG-Based Translation and Human-Augmented RLAIF](https://mlsanity.com/paper/2603.21359/) (2026)
- [Cross-Dialect NER for Bangla Regional Dialects Using Leave-One-Dialect-Out Cross-Validation and Explainable AI](https://mlsanity.com/paper/2609.22536/) (2026)

## Abstract (arXiv)

The Bangla linguistic variety is a fascinating mix of regional dialects that contributes to the cultural diversity of the Bangla-speaking community. Despite extensive study into translating Bangla to English, English to Bangla, and Banglish to Bangla in the past, there has been a noticeable gap in translating Bangla regional dialects into standard Bangla. In this study, we set out to fill this gap by creating a collection of 32,500 sentences, encompassing Bangla, Banglish, and English, representing five regional Bangla dialects. Our aim is to translate these regional dialects into standard Bangla and detect regions accurately. To tackle the translation and region detection tasks, we propose two novel models: DialectBanglaT5 for translating regional dialects into standard Bangla and DialectBanglaBERT for identifying the dialect's region of origin. DialectBanglaT5 demonstrates superior performance across all dialects, achieving the highest BLEU score of 71.93, METEOR of 0.8503, and the lowest WER of 0.1470 and CER of 0.0791 on the Mymensingh dialect. It also achieves strong ROUGE scores across all dialects, indicating both accuracy and fluency in capturing dialectal nuances. In parallel, DialectBanglaBERT achieves an overall region classification accuracy of 89.02%, with notable F1-scores of 0.9241 for Chittagong and 0.8736 for Mymensingh, confirming its effectiveness in handling regional linguistic variation. This is the first large-scale investigation focused on Bangla regional dialect translation and region detection. Our proposed models highlight the potential of dialect-specific modeling and set a new benchmark for future research in low-resource and dialect-rich language settings.