Papers by Happy Mittal
Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes (2023.acl-industry)
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| Challenge: | E-commerce websites often don’t label or mislabel attributes of products . |
| Approach: | They propose a multi-modal product attribute generation system that extracts product attributes from the product pages of eCommerce stores by using both text and images. |
| Outcome: | The proposed model improves the recall@90P accuracy by 10.16% and 6.9 from the state-of-the-art models. |
MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering (2025.naacl-industry)
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| Challenge: | Question Answering (QA) and Visual Question Answers (VQA) are well-studied problems in the language and vision domain. |
| Approach: | They propose a question-answer generation framework that learns attention across multiple sources and decodes this information for robust and unbiased answer generation. |
| Outcome: | The proposed framework can handle thousands of question types and scale to scale. |
Distantly Supervised Transformers For E-Commerce Product QA (2021.naacl-main)
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| Challenge: | e-commerce services often provide an instant QA system on product pages . however, user queries and CQA pairs differ significantly in language characteristics . |
| Approach: | They propose a transformer-based instant question answering system on product pages . for each user query, relevant community question answer (CQA) pairs are retrieved . their framework is able to scale to large e-commerce QA traffic . |
| Outcome: | The proposed model outperforms syntactic and semantic baselines on user queries and training with CQA pairs. |