Papers by Vivek Sembium
KD-Boost: Boosting Real-Time Semantic Matching in E-commerce with Knowledge Distillation (2023.emnlp-industry)
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| Challenge: | Existing SOTA techniques for semantic matching are mostly based on Siamese networks. |
| Approach: | They propose a novel knowledge distillation algorithm designed for real-time semantic matching . they train low latency accurate student models by leveraging soft labels from a teacher model . |
| Outcome: | The proposed algorithm outperforms teacher and SOTA knowledge distillation benchmarks on e-commerce datasets. |
CoMix: Guide Transformers to Code-Mix using POS structure and Phonetics (2023.findings-acl)
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| Challenge: | Existing multilingual transformer models lack the ability to intermix words of one language into the structure of another. |
| Approach: | They propose a pretraining approach to improve representation of code-mixed data in transformer models by incorporating phonetic signals, a modified attention mechanism and weak supervision guided generation by parts-of-speech constraints. |
| Outcome: | The proposed model improves performance across four code-mixed tasks and generalizes on out-of-domain translation. |
RTSM: Knowledge Distillation with Diverse Signals for Efficient Real-Time Semantic Matching in E-Commerce (2025.naacl-industry)
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| Challenge: | e-commerce product search is a key component of product discovery and sales in ecommerce . high computational demands of large transformer models pose challenges for their deployment in real-time scenarios. |
| Approach: | They propose a framework for real-time semantic matching that leverages both soft labels from a teacher model and ground truth generated from pairwise query-product and query-query signals. |
| Outcome: | The proposed framework outperforms teacher models and state-of-the-art models on e-commerce datasets. |