Customer reviews usually contain much information about one's online shopping
experience. While positive reviews are beneficial to the stores, negative ones
will largely influence consumers' decision and may lead to a decline in sales.
Therefore, it is of vital importance to carefully and persuasively reply to
each negative review and minimize its disadvantageous effect. Recent studies
consider leveraging generation models to help the sellers respond. However,
this problem is not well-addressed as the reviews may contain multiple aspects
of issues which should be resolved accordingly and persuasively. In this work,
we propose a Multi-Source Multi-Aspect Attentive Generation model for
persuasive response generation. Various sources of information are
appropriately obtained and leveraged by the proposed model for generating more
informative and persuasive responses. A multi-aspect attentive network is
proposed to automatically attend to different aspects in a review and ensure
most of the issues are tackled. Extensive experiments on two real-world
datasets, demonstrate that our approach outperforms the state-of-the-art
methods and online tests prove that our deployed system significantly enhances
the efficiency of the stores' dealing with negative reviews.

本文提出了一种基于多源多方面注意力生成模型的公式，以更具说服力的方式回应消极评论，并懂得自动关注评论中的不同方面，以确保多个问题被充分应对。大量实验结果表明，该方法胜过先前的方法，并且已经在线上测试中证明了提高商家处理消极评论效率的显著性。