Responding E-commerce Product Questions via Exploiting QA Collections and Review Collections and Reviews
Refereed conference paper presented and published in conference proceedings

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AbstractProviding 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 questionbased 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.
Acceptance Date20/08/2018
All Author(s) ListQian Yu, Wai Lam, Zihao Wang
Name of Conference27th International Conference on Computational Linguistics (COLING)
Start Date of Conference20/08/2018
End Date of Conference26/08/2018
Place of ConferenceNew Mexico
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 27th International Conference on Computational Linguistics
Pages2192 - 2203
LanguagesEnglish-United Kingdom

Last updated on 2019-29-11 at 17:13