Aspect-based Sentiment Analysis as Machine Reading Comprehension

Yifei Yang; Hai Zhao

Aspect-based Sentiment Analysis as Machine Reading Comprehension

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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)
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PDF:: https://aclanthology.org/2022.coling-1.217.pdf

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@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

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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.