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

Authors

  • Yue Mao Alibaba Group
  • Yi Shen Alibaba Group
  • Chao Yu Alibaba Group
  • Longjun Cai Alibaba Group

Keywords:

Text Classification & Sentiment Analysis

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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Published

2021-05-18

How to Cite

Mao, Y., Shen, Y., Yu, C., & Cai, L. (2021). A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15), 13543-13551. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17597

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing II