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

arXiv: https://arxiv.org/abs/2004.01935 · HTML: https://mlsanity.com/paper/2004.01935/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://mlsanity.com/paper/2202.13645/) (2022)
- [Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2110.13398/) (2021)
- [Scheduled Multi-task Learning for Neural Chat Translation](https://mlsanity.com/paper/2205.03766/) (2022)
- [Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2506.12991/) (2025)
- [EmoGRACE: Aspect-based emotion analysis for social media data](https://mlsanity.com/paper/2503.15133/) (2025)

## Abstract (arXiv)

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.