Masum Hasan


2021

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Hitting your MARQ: Multimodal ARgument Quality Assessment in Long Debate Video
Md Kamrul Hasan | James Spann | Masum Hasan | Md Saiful Islam | Kurtis Haut | Rada Mihalcea | Ehsan Hoque
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The combination of gestures, intonations, and textual content plays a key role in argument delivery. However, the current literature mostly considers textual content while assessing the quality of an argument, and it is limited to datasets containing short sequences (18-48 words). In this paper, we study argument quality assessment in a multimodal context, and experiment on DBATES, a publicly available dataset of long debate videos. First, we propose a set of interpretable debate centric features such as clarity, content variation, body movement cues, and pauses, inspired by theories of argumentation quality. Second, we design the Multimodal ARgument Quality assessor (MARQ) – a hierarchical neural network model that summarizes the multimodal signals on long sequences and enriches the multimodal embedding with debate centric features. Our proposed MARQ model achieves an accuracy of 81.91% on the argument quality prediction task and outperforms established baseline models with an error rate reduction of 22.7%. Through ablation studies, we demonstrate the importance of multimodal cues in modeling argument quality.

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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

2020

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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.