Papers by Manan Soni
Know What You See: Grounded localization of product components (2026.acl-industry)
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| Challenge: | Existing object detectors treat components as isolated objects, ignoring their structure . a new method, Know What You See, uses textual knowledge to localize components . |
| Approach: | a new method localizes components by grounding them using a textual knowledge base . KWYS improves component localization accuracy by 11% and reduces component hallucinations by 25% . |
| Outcome: | a new method improves component localization accuracy and reduces component hallucinations . the proposed method improve on 1,000 product images across 5 diverse categories . |
Weakly supervised hierarchical multi-task classification of customer questions (2023.acl-industry)
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Jitenkumar Rana, Promod Yenigalla, Chetan Aggarwal, Sandeep Sricharan Mukku, Manan Soni, Rashmi Patange
| Challenge: | Identifying granular and actionable topics from customer questions helps improve the overall customer experience. |
| Approach: | They propose a weakly supervised Hierarchical Multi-task Classification Framework to identify granular topics from customer questions . a clustering based taxonomy creation and data labeling module is used to create taxonomies and labelled data with minimal supervision. |
| Outcome: | The proposed model achieves 13% better accuracy over single-task classification frameworks . it can adapt to constantly evolving taxonomy without need of re-training . |
InsightNet : Structured Insight Mining from Customer Feedback (2023.emnlp-industry)
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Sandeep Sricharan Mukku, Manan Soni, Chetan Aggarwal, Jitenkumar Rana, Promod Yenigalla, Rashmi Patange, Shyam Mohan
| Challenge: | Existing methods for extracting structured insights from reviews suffer from drawbacks . lack of structure, non-standard aspect names, lack of abundant training data limit their effectiveness and applicability. |
| Approach: | They propose a semi-supervised multi-level taxonomy from raw customer reviews and a semantic similarity heuristic approach to generate labelled data. |
| Outcome: | The proposed approach outperforms existing methods in structure, hierarchy and completeness. |