Papers by Ritwik Banerjee
Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse (2024.findings-acl)
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| Challenge: | Existing studies on whataboutism have focused on tracking "what about" phrases, but they neglect the unique challenges to its detection. |
| Approach: | They propose to use attention weights to distinguish the ‘what about’ lexical construct from whataboutism by using Twitter/X and YouTube datasets. |
| Outcome: | The proposed method improves by 4% and 10% over previous state-of-the-art methods in Twitter and YouTube datasets. |
Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies (2025.emnlp-main)
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| Challenge: | Past studies have focused on fully personalized (or idiosyncratic) models for atypical speech . past studies focused on idiotic models, but current approaches focus on generalizing and handling idiomatic patterns . |
| Approach: | They compare four models that generalize and handle idiosyncrasy to find atypical speech . they find the dysarthric-idios-ync model performs better than the idioconic approach . |
| Outcome: | The proposed model generalizes and handles idiosyncrasy better than the idiocy model . the model requires less personalized data and reduces word error rate from 71% to 32% . |
Querying Across Genres for Medical Claims in News (2020.emnlp-main)
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| Challenge: | Using cross-genre query-based biomedical information retrieval, we find the research publication that supports the primary claim made in a news article. |
| Approach: | They propose a query-based biomedical information retrieval task where the goal is to find the research publication that supports the primary claim made in a news article. |
| Outcome: | The proposed approach compares classical IR with more recent transformer-based models and shows that it is feasible but requires domain-specific knowledge. |
Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks (2025.acl-long)
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| Challenge: | Existing classifiers for detecting deviant language often come with significant computational cost and high data demands. |
| Approach: | They propose a class-disstillation paradigm that targets the core challenge: distilling a small, well-defined target class from a heterogeneous background. |
| Outcome: | The proposed training paradigm outperforms baselines and large language models on three benchmarks. |