Papers by Sumegh Roychowdhury
Data-Efficient Methods For Improving Hate Speech Detection (2023.findings-eacl)
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
| Approach: | They propose an input-level data augmentation technique EasyMix to improve hate speech detection in english and multilingual datasets. |
| Outcome: | The proposed method improves the performance across english and multilingual datasets by 1% and 2-8%. |
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
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| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
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. |
Representation Learning for Conversational Data using Discourse Mutual Information Maximization (2022.naacl-main)
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Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal
| Challenge: | Existing language modeling pretraining objectives do not take structural information of conversational text into account. |
| Approach: | They propose a structure-aware Mutual Information based loss-function DMI for training dialog-representation models that captures the inherent uncertainty in response prediction. |
| Outcome: | The proposed model outperforms strong baseline models on nine diverse tasks. |
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers (2025.emnlp-main)
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Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya, Shailendra Agarwal, Vijay Huddar
| Challenge: | generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, a trade-off that remains unexplored in the transformer era. |
| Approach: | They propose to evaluate generative and discriminative architectures for text classification using a generative model that learns the conditional probability distribution P (y|x) generative models are known to work better in low-data settings, giving rise to the classical 'two regimes' phenomenon for classification. |
| Outcome: | The proposed models show that the classical 'two regimes' manifests distinctly across different architectures and training paradigms. |
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques (2024.findings-acl)
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Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Pattisapu Priyatam, Anish Bhanushali, Prasanna Srinivasa Murthy
| Challenge: | Ordinal classification (OC) is a key task in natural language processing with applications in various domains such as sentiment analysis, rating prediction, and more. |
| Approach: | They propose to tackle ordinal classification (OC) through the implicit semantics of the labels . they propose to use a classical explicit approach and an implicit approach that organically engages the semantics. |
| Outcome: | The proposed methods are based on pre-trained language models and offer strategic recommendations based upon specific settings. |