Papers by Kazi Hasan
A Multilingual Reading Comprehension System for more than 100 Languages (2020.coling-demos)
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Anthony Ferritto, Sara Rosenthal, Mihaela Bornea, Kazi Hasan, Rishav Chakravarti, Salim Roukos, Radu Florian, Avi Sil
| Challenge: | Recent advances in open domain question answering (QA) have focused on machine reading comprehension (MRC) |
| Approach: | They propose a multilingual machine reading comprehension (MRC) demo which can answer questions in over 100 languages. |
| Outcome: | The proposed system can answer questions in over 100 languages and integrates with IBM Watson's machine translation widget to improve language accessibility. |
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 . |