A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis

• URL: http://arxiv.org/abs/2101.00816v2
• Date: Wed, 7 Apr 2021 02:49:57 GMT
• Title: A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis
• Authors: Yue Mao, Yi Shen, Chao Yu, Longjun Cai
• Abstract summary: Aspect based sentiment analysis involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Previous approaches fail to solve all subtasks in a unified end-to-end framework. We construct two machine reading comprehension problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing.
• Score: 9.587513675287829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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