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Inf. Syst."],"published-print":{"date-parts":[[2021,4,30]]},"abstract":"<jats:p>In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of<jats:italic>product-aware answer generation<\/jats:italic>, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.<\/jats:p><jats:p>However,<jats:italic>safe answer problems<\/jats:italic>(i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the<jats:italic>Meaningful Product Answer Generator<\/jats:italic>(<jats:italic>MPAG<\/jats:italic>), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct<jats:italic>reasoning among these reviews<\/jats:italic>. We then employ a prototype reader consisting of comprehensive matching to extract the<jats:italic>answer skeleton<\/jats:italic>from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.<\/jats:p>","DOI":"10.1145\/3432689","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T17:09:57Z","timestamp":1612372197000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Meaningful Answer Generation of E-Commerce Question-Answering"],"prefix":"10.1145","volume":"39","author":[{"given":"Shen","family":"Gao","sequence":"first","affiliation":[{"name":"Peking University"}]},{"given":"Xiuying","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Zhaochun","family":"Ren","sequence":"additional","affiliation":[{"name":"Shandong University"}]},{"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Renmin University of China and Peking University"}]}],"member":"320","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","volume":"16","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , et\u00a0al. 2016 . 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