Papers by Rifat Shahriyar
CoDesc: A Large Code–Description Parallel Dataset (2021.findings-acl)
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Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md. Mahim Anjum Haque, Tahmid Hasan, Wasi Ahmad, Anindya Iqbal, Rifat Shahriyar
| Challenge: | Existing models for natural language and programming languages are lagging behind due to a lack of large datasets and benchmarks. |
| Approach: | They present a large parallel dataset of Java methods and natural language descriptions that is used to train deep neural models. |
| Outcome: | The proposed dataset improves code summarization and code search by 22% and opens up possibilities for pretrained language models for Java. |
An Empirical Study on the Characteristics of Bias upon Context Length Variation for Bangla (2024.findings-acl)
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| Challenge: | Language models exhibit various social biases due to widespread usage. |
| Approach: | They extend existing methods for measuring gender bias in Bangla by examining context length variation. |
| Outcome: | The proposed method relies on context length variation, highlighting the need for nuanced considerations in Bangla bias analysis. |
BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset (2022.aacl-short)
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| Challenge: | Bangla is considered a low resource language in terms of language processing. |
| Approach: | They propose a high-quality synthetic Bangla Paraphrase dataset curated by a novel filtering pipeline. |
| Outcome: | The proposed pipeline ensures quality by preserving both semantics and diversity, making it particularly useful to enhance other Bangla datasets. |
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021.findings-acl)
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Tahmid Hasan, Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | XL-Sum dataset covers 44 languages ranging from low to high-resource . Xl-SUM is highly abstractive, concise, and of high quality . |
| Approach: | They present a dataset comprising 1 million professionally annotated article-summary pairs from BBC . they fine-tune a pretrained multilingual model with XL-Sum and experiment on multilingual and lowresource tasks. |
| Outcome: | The proposed dataset is highly abstractive, concise, and of high quality . it shows higher scores on 10 languages than similar datasets compared to monolingual ones . |
Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation (2020.emnlp-main)
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Tahmid Hasan, Abhik Bhattacharjee, Kazi Samin, Masum Hasan, Madhusudan Basak, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | despite being the seventh most widely spoken language, Bengali has received little attention in machine translation due to being low in resources. |
| Approach: | They propose a customized sentence segmenter for Bengali and two new methods for parallel corpus creation on low-resource setups. |
| Outcome: | The proposed method improves Bengali-English parallel corpus by 9 BLEU over previous approaches . the results will pave the way for future research on Bengali and other low-resource languages . |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla (2022.findings-naacl)
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Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | Bangla is a widely spoken yet low-resource language in the NLP literature. |
| Approach: | They propose a BERT-based natural language understanding model pretrainable in Bangla, a widely spoken yet low-resource language in the NLP literature. |
| Outcome: | The proposed model outperforms multilingual and monolingual models on four NLU tasks covering text classification, sequence labeling, and span prediction. |
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs (2023.acl-long)
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| Challenge: | a large-scale cross-lingual summarization dataset is available for free . a cross-linguistic summarizing model can be trained in any target language . |
| Approach: | They propose a multistage data sampling algorithm to train a cross-lingual summarization model capable of summarizing an article in any target language. |
| Outcome: | The proposed model outperforms baseline models on ROUGE and LaSE. |
Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages (2025.emnlp-main)
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| Challenge: | Encoder transformer models compress information from all tokens into a single [CLS] token to represent global context. |
| Approach: | They propose a 1-D convolution module that augments token representations with multi-scale local features to improve performance. |
| Outcome: | Experiments on five diverse tasks show that the proposed framework outperforms baseline models by 1% to 14% while maintaining efficiency. |
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla (2023.findings-eacl)
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| Challenge: | 'BanglaNLG' is a comprehensive benchmark for evaluating natural language generation models in Bangla, a widely spoken yet low-resource language. |
| Approach: | They propose to aggregate six conditional text generation tasks under the BanglaNLG benchmark and introduce a new dataset on dialogue generation in the process. |
| Outcome: | The proposed model outperforms several multilingual models by 9% absolute gain and 32% relative gain on all of these tasks. |