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        <title>Responding E-commerce Product Questions via Exploiting QA Collections and Reviews</title>
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    <abstract>Providing instant responses for product questions in E-commerce sites can significantly improve satisfaction of potential consumers. We propose a new framework for automatically responding product questions newly posed by users via exploiting existing QA collections and review collections in a coordinated manner. Our framework can return a ranked list of snippets serving as the automated response for a given question, where each snippet can be a sentence from reviews or an existing question-answer pair. One major subtask in our framework is question-based response review ranking. Learning for response review ranking is challenging since there is no labeled response review available. The collection of existing QA pairs are exploited as distant supervision for learning to rank responses. With proposed distant supervision paradigm, the learned response ranking model makes use of the knowledge in the QA pairs and the corresponding retrieved review lists. Extensive experiments on datasets collected from a real-world commercial E-commerce site demonstrate the effectiveness of our proposed framework.</abstract>
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%0 Conference Proceedings
%T Responding E-commerce Product Questions via Exploiting QA Collections and Reviews
%A Yu, Qian
%A Lam, Wai
%A Wang, Zihao
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F yu-etal-2018-responding
%X Providing instant responses for product questions in E-commerce sites can significantly improve satisfaction of potential consumers. We propose a new framework for automatically responding product questions newly posed by users via exploiting existing QA collections and review collections in a coordinated manner. Our framework can return a ranked list of snippets serving as the automated response for a given question, where each snippet can be a sentence from reviews or an existing question-answer pair. One major subtask in our framework is question-based response review ranking. Learning for response review ranking is challenging since there is no labeled response review available. The collection of existing QA pairs are exploited as distant supervision for learning to rank responses. With proposed distant supervision paradigm, the learned response ranking model makes use of the knowledge in the QA pairs and the corresponding retrieved review lists. Extensive experiments on datasets collected from a real-world commercial E-commerce site demonstrate the effectiveness of our proposed framework.
%U https://aclanthology.org/C18-1186
%P 2192-2203

Markdown (Informal)

[Responding E-commerce Product Questions via Exploiting QA Collections and Reviews](https://aclanthology.org/C18-1186) (Yu et al., COLING 2018)

• Responding E-commerce Product Questions via Exploiting QA Collections and Reviews (Yu et al., COLING 2018)

ACL

• Qian Yu, Wai Lam, and Zihao Wang. 2018. Responding E-commerce Product Questions via Exploiting QA Collections and Reviews. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2192–2203, Santa Fe, New Mexico, USA. Association for Computational Linguistics.