  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/.
