BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
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DOI：
10.18653/v1/n19-1242
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Hu Xu;Bing Liu;Lei Shu;Philip S. Yu
中科院分区：
文献类型：
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作者：
Hu Xu;Bing Liu;Lei Shu;Philip S. Yu
问答在电子商务中起着重要的作用，因为它允许潜在客户主动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。受最近成功的机器阅读理解（MRC）的正式文件，本文探讨了潜在的客户评论变成一个大的知识来源，可以用来回答用户的问题。我们称这个问题为复习阅读理解（RRC）。据我们所知，目前还没有关于RRC的工作。在这项工作中，我们首先构建了一个名为ReviewRC的RRC数据集，该数据集基于一个流行的基于方面的情感分析基准。由于ReviewRC对于RRC（以及基于方面的情感分析）的训练示例有限，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，以增强RRC BERT的微调性能。为了展示该方法的通用性，所提出的后训练也被应用于其他一些基于评论的任务，如基于方面的情感分析中的方面提取和方面情感分类。实验结果表明，所提出的后训练是非常有效的。
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.