An Iterative Knowledge Transfer Network with Routing for Aspect-based Sentiment Analysis

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 separately or (partially) jointly. However, the semantic interrelationships among all the three subtasks are not well exploited in previous approaches, which restricts their performance. Additionally, the linguistic knowledge from document-level labeled sentiment corpora is usually used in a coarse way for the ABSA. To address these issues, we propose a novel Iterative Knowledge Transfer Network (IKTN) for the end-to-end ABSA. For one thing, to fully exploit the semantic correlations among the three aspect-level subtasks for mutual promotion, the IKTN transfers the task-specific knowledge from any two of the three subtasks to another one by leveraging a specially-designed routing algorithm, that is, any two of the three subtasks will help the third one. Besides, the IKTN discriminately transfers the document-level linguistic knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to benefit the corresponding ones. Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, which significantly outperforms existing state-of-the-art methods.