An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2004.01935v3
• Date: Thu, 2 Sep 2021 02:21:01 GMT
• Title: An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
• Authors: Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu and Jie Zhou
• Abstract summary: We propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. Our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.
• Score: 73.7488524683061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner. However, previous approaches do not well exploit the interactive relations among three subtasks and do not pertinently leverage the easily available document-level labeled domain/sentiment knowledge, which restricts their performances. To address these issues, we propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing, through the interactive correlations between the ABSA subtasks, our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm, that is, any two of the three subtasks will help the third one. For another, our IMKTN pertinently transfers the document-level knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to further enhance the corresponding performance. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.

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