Reading Customer Reviews to Answer Product-related Questions

The e-commerce websites are ready to build the community question answering (CQA) service, as it can facilitate questioners (potential buyers) to obtain satisfying answers from experienced customers and furthermore stimulate consumption. Given that more than 50% product-related questions only anticipate a binary response (i.e., “Yes” or “No”), the research on productrelated question answering (PQA), which aims to automatically provide instant and correct replies to questioners, emerges rapidly. The mainstream approaches on PQA generally employ customer reviews as the evidence to help predict answers to the questions which are product-specific and concerned more about subjective personal experiences. However, the supportive features either extracted by heuristic rules or acquired from unsupervised manners are not able to perform well on PQA. In this paper, we contribute an end-to-end neural architecture directly fed by the raw text of productrelated questions and customer reviews to predict the answers. Concretely, it teaches machines to generate and to synthesize multiple question-aware review representations in a reading comprehension fashion to make the final decision. We also extract a real-world dataset crawled from 9 categories in Amazon.com for PQA to assess the performance of our neural reading architecture (NRA) and other mainstream approaches such as COR-L [12], MOQA [12], and AAP [21]. Experimental results show that our NRA sets up a new state-of-theart performance on this dataset, significantly outperforming existing algorithms.

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