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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.217.pdf
- Code
- yangyifei729/mrc4absa