Parag Dutta


2022

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CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection
Souvic Chakraborty | Parag Dutta | Sumegh Roychowdhury | Animesh Mukherjee
Findings of the Association for Computational Linguistics: NAACL 2022

The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, their proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content. Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using User Anchored self-supervision and contextual regularization. Our proposed approach secures ~1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular English language social networking datasets.

2021

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Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra | Parag Dutta | Shubham Gupta | Mohammed Abdul Qaathir | Ambedkar Dukkipati
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. Our proposed approach, Active2 Learning (A2L), actively adapts to the deep learning model being trained to eliminate such redundant examples chosen by an AL strategy. We show that A2L is widely applicable by using it in conjunction with several different AL strategies and NLP tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by 3-25% on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.