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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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
Export citation
@inproceedings{yang-zhao-2022-aspect,
title = "Aspect-based Sentiment Analysis as Machine Reading Comprehension",
author = "Yang, Yifei and
Zhao, Hai",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.217",
pages = "2461--2471",
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.",
}
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%0 Conference Proceedings %T Aspect-based Sentiment Analysis as Machine Reading Comprehension %A Yang, Yifei %A Zhao, Hai %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F yang-zhao-2022-aspect %X 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. %U https://aclanthology.org/2022.coling-1.217 %P 2461-2471
Markdown (Informal)
[Aspect-based Sentiment Analysis as Machine Reading Comprehension](https://aclanthology.org/2022.coling-1.217) (Yang & Zhao, COLING 2022)
- Aspect-based Sentiment Analysis as Machine Reading Comprehension (Yang & Zhao, COLING 2022)
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.