  Aspect sentiment triplet extraction (ASTE), which aims to identify aspects
from review sentences along with their corresponding opinion expressions and
sentiments, is an emerging task in fine-grained opinion mining. Since ASTE
consists of multiple subtasks, including opinion entity extraction, relation
detection, and sentiment classification, it is critical and challenging to
appropriately 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 this
challenge. Specifically, we devise three types of queries, including
non-restrictive extraction queries, restrictive extraction queries and
sentiment classification queries, to build the associations among different
subtasks. Furthermore, considering that an aspect sentiment triplet can derive
from either an aspect or an opinion expression, we design a bidirectional MRC
structure. One direction sequentially recognizes aspects, opinion expressions,
and sentiments to obtain triplets, while the other direction identifies opinion
expressions first, then aspects, and at last sentiments. By making the two
directions complement each other, our framework can identify triplets more
comprehensively. To verify the effectiveness of our approach, we conduct
extensive experiments on four benchmark datasets. The experimental results
demonstrate that BMRC achieves state-of-the-art performances.
