A Joint Training Dual MRC Framework for Aspect Based Sentiment Analysis
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 ofthe art methods. Introduction Aspect based sentiment analysis (ABSA)1 is an important research area in natural language processing. Consider the example in Figure 1, in the sentence "The ambience was nice, but the service was not so great." the aspect terms (AT) are "ambience/service" and the opinion terms (OT) are "nice/not so great" Traditionally, there exist three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect level sentiment classification. Recent research works aim to do a combination of two subtasks and have achieved great progress. For example, they extract (AT, OT) pairs, or extract ATs with corresponding sentiment polarities (SP) More recently, some work that aims to do all related subtasks in ABSA with a unified framework has raised increasing interests. For convenience, we assume the following abbreviations of ABSA subtasks as illustrated in Figure 1:
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