MODEL LEARNING SEMANTIC QA RELEVANCE

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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.

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