论文标题
通过多个语义关系建模的产品相关问题的答案排名
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling
论文作者
论文摘要
现在，许多电子商务网站都提供特定于产品的问答平台，以供用户通过在在线购物期间发布和回答问题来相互通信。但是，普通用户提供的多个答案通常在其质量方面各不相同，因此需要适当地对每个问题进行适当的排名，以提高用户满意度。可以观察到，产品评论通常为给定问题提供有用的信息，因此可以帮助排名过程。在本文中，我们研究了与产品相关问题的答案排名问题，相关评论被视为可以利用以促进排名的辅助信息。我们提出了一个名为Muse的答案排名模型，该模型仔细地模拟了问题，答案和相关评论之间的多个语义关系。具体来说，缪斯（Muse）构建了一个多语义关系图，每个问题，每个答案以及每个评论片段都是节点。然后，定制的图形卷积神经网络旨在显式地对问题和答案之间的语义相关，答案之间的内容一致性以及答案和评论之间的文本需要建模。关于三个产品类别的现实世界电子商务数据集的广泛实验表明，我们提出的模型在相关的答案排名任务上实现了卓越的性能。
Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. We propose an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph with the question, each answer, and each review snippet as nodes. Then a customized graph convolutional neural network is designed for explicitly modeling the semantic relevance between the question and answers, the content consistency among answers, and the textual entailment between answers and reviews. Extensive experiments on real-world E-commerce datasets across three product categories show that our proposed model achieves superior performance on the concerned answer ranking task.
