| 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. |
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| Challenge: | eschewing separate architecture and training for knowledge-intensive tasks is cumbersome . end-to-end training only based on supervision from the end task is awkward . |
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| Challenge: | Existing work on product question answering systems focuses mainly on English, but in practice there is need to support multiple customer languages while leveraging product information available in English. |
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| Challenge: | Product question answering (PQA) aims to automatically provide instant responses to customer’s questions in E-commerce platforms. |
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Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
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| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
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Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering (2023.acl-long)
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Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan
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