Papers by Dibyanayan Bandyopadhyay
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
| Approach: | They propose an effective transfer learning approach for cross-lingual NLI . they perform experiments on English-Hindi language pairs in cross-linguistic setting . |
| Outcome: | The proposed model improves the baseline model by 10% over the state-of-the-art model. |
CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge (2024.lrec-main)
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| Challenge: | Existing studies on detecting offensive memes have focused on identifying them as implicit and explicit . detecting memes requires contextual knowledge, but there is no such dataset for the code-mixed Hindi-English domain. |
| Approach: | They propose an end-to-end multitask model that integrates contextual knowledge and psycho-linguistic knowledge to detect offensive memes. |
| Outcome: | The proposed model is able to detect offensive memes and explicit memes in a large-scale dataset. |
Seeing Through VisualBERT: A Causal Adventure on Memetic Landscapes (2024.findings-emnlp)
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| Challenge: | Existing models for detecting offensive memes lack transparency and are often unreliability in safety-critical applications. |
| Approach: | They propose a framework that uses a Structural Causal Model to predict the class of an input meme based on meme input and causal concepts, allowing for transparent interpretation. |
| Outcome: | The proposed framework is able to predict class of an input meme based on meme input and causal concepts, allowing for transparent interpretation. |
Transforming Code Understanding: Clustering-Based Retrieval for Improved Summarization in Domain-Specific Languages (2025.coling-industry)
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Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee, Aryan Sahoo, Saswati Dana, Palanivel Kodeswaran, Sayandeep Sen, Asif Ekbal, Dinesh Garg
| Challenge: | Existing natural language summaries of domain-specific languages are limited due to their recency and complexity. |
| Approach: | They propose a clustering-based technique to retrieve in-context examples that are semantically closer to the test example and propose eBPF prompt generation technique that yields superior-quality code summary generation. |
| Outcome: | The proposed method improves the eBPF code summarization accuracy by 12.9 BLEU points over other prompting techniques. |