Papers by Nikesh Garera
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. |
Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce (2023.acl-industry)
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| Challenge: | Recent years have witnessed the popularity of end-to-end ASR models, which have demonstrated higher accuracy compared to traditional pipelines with separate acoustic, pronunciation, and language models. |
| Approach: | They build accurate LSTM, attention and CTC based streaming ASR models for large-scale Hinglish voice search. |
| Outcome: | The proposed model achieves a word error rate (WER) of 3.69% without EOS and 4.78% with EOS, with 1300 ms (46.64%) reduction in latency. |
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. |
Large-scale Machine Translation for Indian Languages in E-commerce under Low Resource Constraints (2022.emnlp-industry)
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| Challenge: | We have deployed reliable and precise large-scale machine translation systems for several Indian regional languages. |
| Approach: | They develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. |
| Outcome: | The proposed model improves over iterations for English to Hindi and for other languages. |
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English (2022.emnlp-industry)
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| Challenge: | Experimental results show that an E2E S2I model outperforms a conventional pipeline by 27% on the F1 score . multi-component pipelines require large annotated datasets, have higher latency, and have complex deployment. |
| Approach: | They propose an end-to-end (E2E) S2I model that uses pre-trained automatic speech recognition with slight modification and fine-tuning on small annotated datasets. |
| Outcome: | The proposed model outperforms a conventional pipeline on an annotated dataset by 27% on the F1 score. |
Deploying Unified BERT Moderation Model for E-Commerce Reviews (2022.emnlp-industry)
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| Challenge: | Product reviews and ratings are an integral part of the shopping experience to build trust among users. |
| Approach: | They propose a system and a model for automated moderation of user-generated content that can be pre-trained with monolingual data to cope with the domain gap. |
| Outcome: | The proposed model achieves 4.78% increase in F1 score with less labelled data and 2.57% increase in score on the review data compared to the publicly available models. |
Domain-specific transformer models for query translation (2023.acl-industry)
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| Challenge: | In domains such as Grocery, users prefer to buy certain brands of products . a large non-English speaking population makes it difficult to translate code-mix queries . |
| Approach: | They propose a model to preserve/correct Grocery brand names while translating context words . they propose to use a dataset of popular Groceries brand names to train the model . |
| Outcome: | The proposed model preserves/corrects Grocery brand names while translating context words . it is tested with a large non-English speaking population and is deployed in production . |
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. |
Label efficient semi-supervised conversational intent classification (2023.acl-industry)
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| Challenge: | A conversational chatbot can answer pre-purchase questions and post-purchase queries to provide a seamless shopping experience. |
| Approach: | They propose a semi-supervised learning approach for label-efficient intent classification using a small labeled corpus and large unlabeled query data to train a transformer model. |
| Outcome: | The proposed approach significantly improves over the baseline, even with a limited labeled set. |
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. |