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

TL;DR

This paper introduces an end-to-end iterative network that leverages interrelated subtasks and document-level knowledge to improve aspect-based sentiment analysis performance.

Contribution

It proposes a novel IMKTN model that facilitates knowledge transfer among subtasks and from document-level data, enhancing ABSA accuracy.

Findings

Outperforms existing methods on three benchmark datasets.

Effectively transfers knowledge between subtasks and from document-level data.

Demonstrates significant improvements in aspect-level sentiment classification.

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.