  Aspect based sentiment analysis (ABSA) involves three fundamental subtasks:
aspect term extraction, opinion term extraction, and aspect-level sentiment
classification. Early works only focused on solving one of these subtasks
individually. Some recent work focused on solving a combination of two
subtasks, e.g., extracting aspect terms along with sentiment polarities or
extracting the aspect and opinion terms pair-wisely. More recently, the triple
extraction task has been proposed, i.e., extracting the (aspect term, opinion
term, sentiment polarity) triples from a sentence. However, previous approaches
fail to solve all subtasks in a unified end-to-end framework. In this paper, we
propose a complete solution for ABSA. We construct two machine reading
comprehension (MRC) problems and solve all subtasks by joint training two
BERT-MRC models with parameters sharing. We conduct experiments on these
subtasks, and results on several benchmark datasets demonstrate the
effectiveness of our proposed framework, which significantly outperforms
existing state-of-the-art methods.
