Papers by Parag Dutta
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)
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| Challenge: | Existing approaches to deep learning for NLP require large amounts of labeled data. |
| Approach: | They propose an approach that iteratively selects a small number of examples for expert annotation based on their estimated utility in training the model. |
| Outcome: | The proposed approach reduces the data requirements of state-of-the-art AL strategies by 3-25% on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead. |
CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection (2022.findings-naacl)
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| Challenge: | Recent advances in NLP have often been used to mitigate the spread of hate speech and cyber-bullying on social networks. |
| Approach: | They propose a framework for hate speech detection using user-anchored self-supervision and contextual regularization to learn better representations of hateful content. |
| Outcome: | The proposed approach secures 1-12% improvement in test set metrics over best performing approaches on two types of tasks and multiple popular English language social networking datasets. |