Papers by Tanmoy Mukherjee
Credal Concept Bottleneck Models for Epistemic–Aleatoric Uncertainty Decomposition (2026.acl-long)
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| Challenge: | Existing models face challenges when dealing with uncertainty. |
| Approach: | They propose a framework that decomposes concept uncertainty by construction . epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks disagreement . |
| Outcome: | The proposed framework decomposes concept uncertainty by construction . epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks disagreement . |
Explanation Quality Assessment as Ranking with Listwise Rewards (2026.findings-acl)
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| Challenge: | a new approach to explanation quality assessment is to rank explanations by relative quality . standard reward objectives do not preserve graded distinctions well enough for policy optimization . |
| Approach: | They reformulate explanation quality assessment as a ranking problem instead of a generation problem . they train listwise and pairwise ranking models to preserve ordinal structure . |
| Outcome: | The proposed model outperforms regression on score separation and performance on listwise and pairwise models. |
Learning Unsupervised Word Translations Without Adversaries (D18-1)
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| Challenge: | Current methods for word translation are based on adversarial models and suffer from instability and hyper-parameter sensitivity. |
| Approach: | They propose a statistical dependency-based approach to bilingual dictionary induction that is unsupervised and introduces no adversary. |
| Outcome: | The proposed method outperforms adversarial alternatives and is much easier to train. |
Detecting Harmful Memes and Their Targets (2021.findings-acl)
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Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing body of research on meme analysis has focused on detecting harmful memes and their social entities . a meme is a form of content that is often harmless and designed to look funny . but its multimodal nature and camouflaged semantics make its analysis challenging . |
| Approach: | They propose to use multimodal models to detect harmful memes and identify social entities that harmful meme targets. |
| Outcome: | The proposed model can detect harmful memes and the social entities they target . the proposed model lacks the appropriate contexts and is poorly validated . |