论文标题
在电子商务中，意见感知的答案生成用于评论驱动的问题答案
Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
论文作者
论文摘要
与产品相关的问答（QA）是电子商务中的一项重要但具有挑战性的任务。它导致对自动评论驱动的质量保证的需求很大，该质量质量质量质量质量请立即根据不同的产品评论提供对用户提出的问题的即时回答。然而，在当前基于一代的评论驱动的QA研究中，关于回答这些特定于产品的问题至关重要的产品评论中有关个人意见的丰富信息至关重要。从评论中利用意见信息以促进意见的答案生成时，面临两个主要挑战：（i）共同对问题和评论之间的自以为是且相互关联的信息来捕获答案生成的重要信息，（ii）汇总多样化的意见信息，以发现给定问题的共同意见。在本文中，我们通过以统一模型共同学习答案和意见采矿任务来解决意见感知的答案。提出了两种意见融合策略，即静态和动态融合，以提炼和汇总从意见采矿任务中学到的重要意见信息到答案生成过程。然后，使用多视图指针网络网络来为给定产品相关的问题生成意见的答案。实验结果表明，我们的方法在现实世界电子商务QA数据集中取得了卓越的性能，并有效地产生了自以为是且内容丰富的答案。
Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question. In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model. Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process. Then a multi-view pointer-generator network is employed to generate opinion-aware answers for a given product-related question. Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets, and effectively generate opinionated and informative answers.
