Papers by Baban Gain
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. |
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (2026.acl-long)
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| Challenge: | Spontaneous speech is rarely fluent, and disfluencies can degrade readability and reliability . a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning . |
| Approach: | They propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens . they add a contrastive learning objective that penalizes the reproduction of disfluency tokens. |
| Outcome: | The proposed model improves readability and reliability of ASR transcripts in three languages . disfluencies can cause misinterpretations, incoherent responses, poor user experience . |
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. |
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards (2023.findings-emnlp)
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Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah
| Challenge: | Creating a synthetic parallel corpus from noisy data is also difficult due to its noisy nature. |
| Approach: | They propose a training methodology that fine-tunes the NMT system only using source-side data to balance adequacy and fluency. |
| Outcome: | The proposed method surpasses the MLE-based fine-tuning approach by achieving a 1.9 BLEU improvement. |