Papers by Rahul Bhagat
CUPID: Curriculum Learning Based Real-Time Prediction using Distillation (2023.acl-industry)
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| Challenge: | Relevance in E-commerce Product Search is crucial for providing customers with accurate results that match their query intent. |
| Approach: | They propose a curriculum learning based real-time relevance prediction using distillation . they propose e-commerce search systems that use transformers to predict relevance . |
| Outcome: | The proposed model improves on english and Arabic in a bi-lingual relevance prediction task while maintaining low evaluation latency on CPUs. |
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores (2022.naacl-industry)
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Ashutosh Joshi, Shankar Vishwanath, Choon Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May
| Challenge: | Existing methods to augment training data for e-commerce stores using behavioral data are limited in low-traffic stores . eXtreme multi-label classification systems require large amounts of customer behavior data . |
| Approach: | They propose a technique that augments behavioral training data via query reformulation . they use an example semantic matching model from the e-commerce store AL-XMC . |
| Outcome: | The proposed method improves quality of the AL-XMC model over a baseline model. |
Learning to Rewrite Negation Queries in Product Search (2025.coling-industry)
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| Challenge: | Negations in product search are often used to articulate unwanted product features or components. |
| Approach: | They propose a query rewriting approach to enhance product search performance . they use large language models to extract query reawrites from product text . their results pave the way for further research on enhancing search performance of queries with negations . |
| Outcome: | The proposed approach improves search performance by 3.17% for queries with negations. |