The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.

本文提出了一种基于评论的框架，在电子商务中回答有关产品相关问题的研究。我们开发了一种名为 RAGE 的框架，采用多层卷积结构，从评论中提取与问题相关的信息，实现了更快速和准确的回答生成。在两个真实的电子商务数据集上的实验证明了 RAGE 显著优于现有的替代方案，所生成的自然语言回答更准确、更丰富，而且 RAGE 模型的训练和回答生成所需的时间也较少。

电商产品相关问题的评论驱动答案生成