Tahmid Hasan


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BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla
Abhik Bhattacharjee | Tahmid Hasan | Wasi Ahmad | Kazi Samin Mubasshir | Md Saiful Islam | Anindya Iqbal | M. Sohel Rahman | Rifat Shahriyar
Findings of the Association for Computational Linguistics: NAACL 2022

In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed ‘Bangla2B+’) by crawling 110 popular Bangla sites. We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction. In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB). BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models. We are making the models, datasets, and a leaderboard publicly available at https://github.com/csebuetnlp/banglabert to advance Bangla NLP.


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CoDesc: A Large Code–Description Parallel Dataset
Masum Hasan | Tanveer Muttaqueen | Abdullah Al Ishtiaq | Kazi Sajeed Mehrab | Md. Mahim Anjum Haque | Tahmid Hasan | Wasi Ahmad | Anindya Iqbal | Rifat Shahriyar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
Tahmid Hasan | Abhik Bhattacharjee | Md. Saiful Islam | Kazi Mubasshir | Yuan-Fang Li | Yong-Bin Kang | M. Sohel Rahman | Rifat Shahriyar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation
Tahmid Hasan | Abhik Bhattacharjee | Kazi Samin | Masum Hasan | Madhusudan Basak | M. Sohel Rahman | Rifat Shahriyar
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.