Papers by Muthusamy Chelliah
Synthesize, if you do not have: Effective Synthetic Dataset Creation Strategies for Self-Supervised Opinion Summarization in E-commerce (2023.findings-emnlp)
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Tejpalsingh Siledar, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing approaches to generate general and aspect-specific opinion summarization are limited due to their reliance on human-specified aspects and seed words. |
| Approach: | They propose synthetic dataset creation approaches for general and aspect-specific opinion summarization . general opinion summaries struggle to generate faithful to the input reviews, they say . aspect- specific opinion summarisation models are limited due to reliance on human-specified aspects . |
| Outcome: | The proposed approach outperforms existing models on three e-commerce test sets on general and aspect-specific opinion summarization. |
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation (2024.acl-long)
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Tejpalsingh Siledar, Swaroop Nath, Sankara Muddu, Rupasai Rangaraju, Swaprava Nath, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera
| Challenge: | Existing evaluation methods for opinion summarizations lack adequate opinion summary evaluation datasets. |
| Approach: | They propose a dataset that combines 7 dimensions crucial to opinion summaries . they propose OP-I-PROMPT, a dimension-independent prompt, and OP PROMPTS, . |
| Outcome: | The proposed model achieves a Spearman correlation of 0.70 with human judgments, surpassing prior methods. |
Dense Retrieval with Quantity Comparison Intent (2025.findings-acl)
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| Challenge: | Existing sparse and dense retrieval systems fragment numerals and units that express quantities in arbitrary ways. |
| Approach: | They propose a dense retrieval system built around a density multi-vector index . they propose eliciting and exploiting quantities and associated comparison intents . |
| Outcome: | The proposed system is faster and more accurate than popular PLMs on two public and one proprietary e-commerce benchmarks. |
Product Description and QA Assisted Self-Supervised Opinion Summarization (2024.findings-naacl)
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Tejpalsingh Siledar, Rupasai Rangaraju, Sankara Muddu, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Swaprava Nath, Pushpak Bhattacharyya
| Challenge: | Existing methods to generate opinion summarization without supervised training data are limited due to the lack of additional sources. |
| Approach: | They propose a synthetic dataset creation strategy that leverages reviews and additional sources to generate a pseudo-summary. |
| Outcome: | The proposed approach achieves 14.5% improvement in ROUGE-1 F1 over existing models. |
Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce (2025.findings-emnlp)
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Arnav Attri, Anuj Attri, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing research has not explored the joint task of emotion detection and explanatory span identification in e-commerce reviews. |
| Approach: | They propose a joint task unifying Emotion detection and Opinion Trigger extraction (EOT) which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories). |
| Outcome: | The proposed framework surpasses zero-shot and chain-of-thought techniques across e-commerce domains. |
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. |