Aspect-based Sentiment Analysis as Machine Reading Comprehension

Yifei Yang, Hai Zhao


Abstract
Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance.
Anthology ID:
2022.coling-1.217
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2461–2471
Language:
URL:
https://aclanthology.org/2022.coling-1.217
DOI:
Bibkey:
Cite (ACL):
Yifei Yang and Hai Zhao. 2022. Aspect-based Sentiment Analysis as Machine Reading Comprehension. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2461–2471, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Aspect-based Sentiment Analysis as Machine Reading Comprehension (Yang & Zhao, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.217.pdf
Code
 yangyifei729/mrc4absa