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Aspect sentiment triplet extraction (ASTE), which aims to identify aspectsfrom review sentences along with their corresponding opinion expressions andsentiments, is an emerging task in fine-grained opinion mining. Since ASTEconsists of multiple subtasks, including opinion entity extraction, relationdetection, and sentiment classification, it is critical and challenging toappropriately capture and utilize the associations among them. In this paper,we transform ASTE task into a multi-turn machine reading comprehension (MTMRC)task and propose a bidirectional MRC (BMRC) framework to address thischallenge. Specifically, we devise three types of queries, includingnon-restrictive extraction queries, restrictive extraction queries andsentiment classification queries, to build the associations among differentsubtasks. Furthermore, considering that an aspect sentiment triplet can derivefrom either an aspect or an opinion expression, we design a bidirectional MRCstructure. One direction sequentially recognizes aspects, opinion expressions,and sentiments to obtain triplets, while the other direction identifies opinionexpressions first, then aspects, and at last sentiments. By making the twodirections complement each other, our framework can identify triplets morecomprehensively. To verify the effectiveness of our approach, we conductextensive experiments on four benchmark datasets. The experimental resultsdemonstrate that BMRC achieves state-of-the-art performances.

Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction