Papers by Rajdeep Mukherjee
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling (2024.naacl-long)
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Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | Existing methods to automatically annotate relevant numerals (GAAP metrics) occurring in financial documents are not cost-effective nor scalable. |
| Approach: | They propose a generative paradigm for annotating GAAP metrics with XBRL tags using metric metadata and a parameter efficient model using LoRA. |
| Outcome: | The proposed model outperforms baseline models on two financial numeric labeling datasets and outperformed several strong baseline models. |
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation (2022.aacl-main)
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Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh
| Challenge: | Summarization of legal case judgement documents is a challenging problem in Legal NLP. |
| Approach: | They propose to use extractive and abstractive summarization methods to evaluate legal document summarizing systems. |
| Outcome: | The proposed methods have been evaluated on three legal summarization datasets. |
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on Aspect Sentiment Triplet Extraction focus on developing more efficient techniques for the task, but our proposed approach can improve the downstream performance of multiple ABSA tasks simultaneously. |
| Approach: | They propose a novel approach that uses contrastive learning to enhance the ASTE performance by masked sentiments. |
| Outcome: | The proposed approach improves the performance of multiple ABSA tasks simultaneously. |
MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments (2023.emnlp-main)
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| Challenge: | In the context of the Indian judiciary, there is an additional complexity - Indian legal case judgments are mostly written in complex English due to historical reasons, but a significant portion of India's population lacks a strong command of the English language. |
| Approach: | They propose to summarize Indian legal case judgments in English and Hindi by combining the summaries of 3,122 case judgment from Indian courts into one dataset. |
| Outcome: | The proposed dataset compares the summarization methods with other datasets and shows that the proposed approaches perform better than previous approaches. |
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts (2022.emnlp-main)
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Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | ECTSum is a dataset for bullet-point summarization of earnings calls hosted by publicly traded companies. |
| Approach: | They propose a dataset with transcripts of earnings calls and bullet point summaries derived from Reuters articles. |
| Outcome: | The proposed dataset compares transcripts of earnings calls hosted by publicly traded companies with experts-written bullet point summaries derived from Reuters articles . |
DIRECT: Directional Relevance in Conversational Trajectories (2026.eacl-industry)
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| Challenge: | Conversational Agents (Agents) often fail to understand how to start a conversation or what to ask next . a novel approach to recommending highly relevant follow-up question suggestions is proposed . |
| Approach: | They propose a method to recommend highly relevant follow-up question suggestions . they use offline QBs to fetch the most-relevant candidate questions . |
| Outcome: | The proposed system produces a ranked list of highly relevant follow-up question recommendations within 1 sec. |
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for tagging opinion triplets fail to capture the strong interdependence between the three opinion factors, whereas grid tabbing fails to capture span-level semantics while predicting sentiment between an aspect-opinion pair. |
| Approach: | They propose a tagging-free approach to extracting opinion triplets using a pointer network decoding framework that captures the interdependence between the three elements of an opinion triple. |
| Outcome: | The proposed architecture captures the interdependence between the aspect and opinion triplets while predicting their connecting sentiment. |