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    <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.</abstract>
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%0 Conference Proceedings
%T BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
%A Xu, Hu
%A Liu, Bing
%A Shu, Lei
%A Yu, Philip
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F xu-etal-2019-bert
%X 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.
%R 10.18653/v1/N19-1242
%U https://aclanthology.org/N19-1242
%U https://doi.org/10.18653/v1/N19-1242
%P 2324-2335

Markdown (Informal)

[BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://aclanthology.org/N19-1242) (Xu et al., NAACL 2019)

• BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (Xu et al., NAACL 2019)

ACL

• Hu Xu, Bing Liu, Lei Shu, and Philip Yu. 2019. BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2324–2335, Minneapolis, Minnesota. Association for Computational Linguistics.