Papers by Arnab Bhattacharya
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts (2024.findings-acl)
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| Challenge: | Prediction with Explanation is the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context . |
| Approach: | They propose to use an annotated legal judgment prediction corpus to improve models' accuracy . they employ transformer-based models tailored for both general and Indian legal contexts . |
| Outcome: | The proposed system improves the accuracy and explanatory depth of models for legal judgments. |
Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction (2026.acl-srw)
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| Challenge: | Existing approaches to extract relational tuples from text are incomplete and ambiguous . Existing methods rely on predefined schemas to produce t-uples . |
| Approach: | They propose a normalization-first framework that reframes OIE as a structured semantic transformation pipeline . they formalize soundness, completeness, and usefulness as approximate yet verifiable guarantees over extraction quality . |
| Outcome: | The proposed framework aims to make OIE usable for downstream reasoning and machine interpretability. |
Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction (2023.emnlp-main)
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| Challenge: | Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of daughter languages. |
| Approach: | They propose to use a protein language model to train on multiple sequence alignments to train a model on phonological reconstruction. |
| Outcome: | The proposed model outperforms existing models on cognate reflex prediction task. |
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation (2021.acl-long)
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Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, Kripabandhu Ghosh, Shouvik Kumar Guha, Arnab Bhattacharya, Ashutosh Modi
| Challenge: | a system that could assist a judge in predicting the outcome of a case should be explainable. |
| Approach: | They propose to use a corpus of 35k Indian Supreme Court cases annotated with original court decisions to promote research in this area. |
| Outcome: | The proposed system has an accuracy of 78% versus 94% for human legal experts. |
A Likelihood Ratio Test of Genetic Relationship among Languages (2024.naacl-long)
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| Challenge: | Existing tests of significance for bilateral comparisons are infeasible by design or yield false positives when applied to groups of languages or language families. |
| Approach: | They propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in aligned wordlists. |
| Outcome: | The proposed test solves the problem of false positives on some language families. |
HLDC: Hindi Legal Documents Corpus (2022.findings-acl)
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Arnav Kapoor, Mudit Dhawan, Anmol Goel, Arjun T H, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi
| Challenge: | Existing systems that process legal documents are lacking high-quality corpora in low resource languages such as Hindi. |
| Approach: | They propose a Hindi Legal Documents Corpus (HLDC) that contains 900K legal documents in Hindi. |
| Outcome: | The proposed model is based on a corpus of more than 900K legal documents in Hindi. |
Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages (2026.acl-long)
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| Challenge: | Multilingual large language models are expensive to pretrain and suffer from imbalances across languages and datasets. |
| Approach: | They propose a family of Indian language-only autoregressive language models trained on open-source language-specific data for the five most spoken Indian languages. |
| Outcome: | The proposed model outperforms most larger models up to 8B across all five languages. |
NYAYAANUMANA and INLEGALLLAMA: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis (2025.coling-main)
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Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Shivam Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
| Challenge: | In India, a significant backlog of cases burdens the legal system. |
| Approach: | They present a corpus of 7,02,945 preprocessed Indian legal cases compiled for LJP . they use a domain-specific generative large language model tailored to the intricacies of the legal system . |
| Outcome: | The proposed dataset surpasses existing datasets like PredEx and ILDC, and improves prediction accuracy and comprehensible explanations. |
Leveraging LLMs for Bangla Grammar Error Correction: Error Categorization, Synthetic Data, and Model Evaluation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) perform exceedingly well in Natural Language Understanding tasks for many languages including English. |
| Approach: | They propose to use a rule-based noise injection method to create grammatically incorrect sentences . they categorize 12 error classes in Bangla and take a survey of native speakers . |
| Outcome: | The proposed method improves performance of LLMs in Bangla by 3-7 percentage points compared to zero-shot setting . human errors are still superior in error correction, the authors show . |
A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs (2025.findings-acl)
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V.S.D.S.Mahesh Akavarapu, Hrishikesh Terdalkar, Pramit Bhattacharyya, Shubhangi Agarwal, Dr. Vishakha Deulgaonkar, Chaitali Dangarikar, Pralay Manna, Arnab Bhattacharya
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across diverse tasks and languages. |
| Approach: | They focus on named entity recognition and machine translation into English to examine factors affecting cross-lingual zero-shot generalization. |
| Outcome: | The proposed models perform better than fine-tuned baselines on out-of-domain data, but smaller models struggle with niche or abstract entity types. |
LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification (2025.findings-naacl)
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Shubham Kumar Nigam, Tanmay Dubey, Govind Sharma, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
| Challenge: | a lack of large-scale annotated datasets hinders effective training of ML models . despite advances in semantic segmentation, challenges persist in distinguishing between closely related roles . |
| Approach: | They propose a large annotated dataset for semantic segmentation of legal documents . they use a rhetorical role classification model to compare performance against other models . |
| Outcome: | The largest annotated dataset for this task outperforms models relying on sentence-level features. |
Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer (2024.eacl-long)
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| Challenge: | Existing methods for cognate identification are based on distributions of phonemes and make little use of cognacy labels. |
| Approach: | They propose a transformer-based architecture inspired by computational biology for automated cognate detection. |
| Outcome: | The proposed architecture performs better than existing methods with increased supervision. |
BanglaByT5: Byte-Level Modelling for Bangla (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks. |
| Approach: | They propose a byte-level encoder-decoder model specifically tailored for Bangla. |
| Outcome: | The proposed model outperforms existing models in gen-erative and classification tasks and surpasses several multilingual and larger models. |