In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (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 Meaningful Product Answer Generator ( MPAG ), 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 reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton 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.

中文翻译：

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电子商务问答的有意义的答案生成

在电子商务门户中，为产品相关问题生成答案已成为一项至关重要的任务。在本文中，我们专注于产品感知答案生成，它学习从大规模未标记的电子商务评论和产品属性中生成准确而完整的答案。然而，安全答案问题（即神经模型倾向于生成无意义的通用答案）对文本生成任务提出了重大挑战，电子商务问答任务也不例外。为了生成更有意义的答案，在本文中，我们提出了一种新颖的生成神经模型，称为有意义的产品答案生成器(MPAG)，通过考虑产品评论、产品属性和原型答案来缓解安全答案问题。产品评论和产品属性用于提供有意义的内容，而原型答案可以产生更多样化的答案模式。为此，我们提出了一种带有评论推理模块和原型答案阅读器的新颖答案生成器。我们的关键思想是从大规模的评论集合中获得正确的问题感知信息，并学习如何从现有的原型答案中编写出连贯且有意义的答案。更具体地说，我们提出了一种由选择性写入单元组成的读写存储器来进行在这些评论中进行推理. 然后我们使用一个由综合匹配组成的原型阅读器来提取答案骨架从原型答案。最后，我们提出了一个答案编辑器，通过将问题和上述部分作为输入来生成最终答案。在从电子商务平台收集的真实数据集上进行，广泛的实验结果表明，我们的模型在自动指标和人工评估方面都达到了最先进的性能。人工评估还表明，我们的模型可以始终如一地生成具体和正确的答案。