Papers by Bhavuk Singhal

5 papers
Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for an Agglutinative Low-resource Language (2023.emnlp-industry)

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Challenge: Inverse Text Normalisation (ITN) is a textrewriting task that converts verbalized text to written form.
Approach: They propose to use a seq2seq model, a non-autoregressive text editor and a sequence tagger + rules combination to fine-tune three pre-trained neural models.
Outcome: The proposed model improves with bootstrapping and data augmentation, and bootstrap alone shows a percentage improvement of 14.12 %.
TrendPulse: A Simple yet Efficient Framework for Capturing Viral E-Commerce Spikes via LLM-Driven Contextualization (2026.acl-industry)

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Challenge: Modern e-commerce platforms mostly depend on reactive discovery, where products surface only after users search for them.
Approach: They propose a framework that identifies regional search momentum and leverages Large Language Model to transform spikes into semantic trends.
Outcome: The proposed framework shows consistent improvements across multiple business metrics and overall user experience.
Grounded Multimodal In-Context Learning for Product Weight Estimation at Scale in E-commerce (2026.acl-industry)

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Challenge: a large number of e-commerce platforms require manual verification and specialized hardware.
Approach: They propose a multimodal weight estimation framework that uses category-specific exemplars to infer discretized weight buckets.
Outcome: The proposed approach outperforms strong multimodal KNN baselines in accuracy and near-bucket reliability.
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery (2023.findings-emnlp)

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Challenge: Intent-related tasks are typically modeled as separate tasks, but a unified approach is proposed . INTENDD uses an entirely unsupervised contrastive learning strategy for representation learning .
Approach: They propose a unified approach to identifying intents from dialogue utterances . they propose an unsupervised contrastive learning strategy for representation learning .
Outcome: The proposed approach outperforms baselines on three intent-related tasks on multiple datasets.
GeoIndia: A Seq2Seq Geocoding Approach for Indian Addresses (2024.emnlp-industry)

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Challenge: a new geocoding system for Indian addresses addresses is needed for logistics, urban planning and location-based services.
Approach: They propose a geocoding system for Indian addresses using hierarchical H3-cell prediction using a Seq2Seq framework.
Outcome: The proposed system outperforms existing geocoding platforms in accuracy and reliability across multiple Indian states.

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