A Unified Generative Framework for Aspect-based Sentiment Analysis

Hang Yan, Junqi Dai, Tuo Ji, Xipeng Qiu, Zheng Zhang


Abstract
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which leads to various complicated ABSA models while hard to solve these subtasks in a unified framework. In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation. Based on the unified formulation, we exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to-end framework. Extensive experiments on four ABSA datasets for seven subtasks demonstrate that our framework achieves substantial performance gain and provides a real unified end-to-end solution for the whole ABSA subtasks, which could benefit multiple tasks.
Anthology ID:
2021.acl-long.188
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2416–2429
Language:
URL:
https://aclanthology.org/2021.acl-long.188
DOI:
10.18653/v1/2021.acl-long.188
Bibkey:
Cite (ACL):
Hang Yan, Junqi Dai, Tuo Ji, Xipeng Qiu, and Zheng Zhang. 2021. A Unified Generative Framework for Aspect-based Sentiment Analysis. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2416–2429, Online. Association for Computational Linguistics.
Cite (Informal):
A Unified Generative Framework for Aspect-based Sentiment Analysis (Yan et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.188.pdf
Video:
 https://aclanthology.org/2021.acl-long.188.mp4
Code
 yhcc/BARTABSA +  additional community code
Data
ASTE-Data-V2SemEval 2014 Task 4 Sub Task 2