Abstract
Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions.~We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspect-based sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed post-training is highly effective. The datasets and code are available at https://www.cs.uic.edu/~hxu/.
Abstract (translated)
问答在电子商务中起着重要的作用，因为它允许潜在客户积极寻求有关产品或服务的关键信息，以帮助他们做出购买决策。受正式文档机器阅读理解（MRC）最近取得成功的启发，本文探讨了将客户评论转化为可用于回答用户问题的大量知识源的潜力。~我们称之为问题回顾阅读理解（RRC）。据我们所知，目前还没有就RRC开展任何工作。在这项工作中，我们首先构建了一个RRC数据集，称为reviewrc，它基于一个流行的基于方面的情绪分析基准。由于reviewrc对rrc的训练示例有限（也用于基于方面的情感分析），因此我们在流行的语言模型bert上探索了一种新的后训练方法，以提高rrc的bert微调性能。为了说明这一方法的通用性，本文还将其应用于其他基于综述的任务，如基于方面的情绪分析中的方面提取和方面情绪分类。实验结果表明，所提出的岗位培训方法是有效的。数据集和代码可从https://www.cs.uic.edu/~hxu/获取。
URL
https://arxiv.org/abs/1904.02232
