Overview of the SIGHAN 2024 shared task for Chinese dimensional aspect-based sentiment analysis

Lung-Hao Lee, Liang-Chih Yu, Suge Wang, Jian Liao

Abstract
This paper describes the SIGHAN-2024 shared task for Chinese dimensional aspect-based sentiment analysis (ABSA), including task description, data preparation, performance metrics, and evaluation results. Compared to representing affective states as several discrete classes (i.e., sentiment polarity), the dimensional approach represents affective states as continuous numerical values (called sentiment intensity) in the valence-arousal space, providing more fine-grained affective states. Therefore, we organized a dimensional ABSA (shorted dimABSA) shared task, comprising three subtasks: 1) intensity prediction, 2) triplet extraction, and 3) quadruple extraction, receiving a total of 214 submissions from 61 registered participants during evaluation phase. A total of eleven teams provided selected submissions for each subtask and seven teams submitted technical reports for the subtasks. This shared task demonstrates current NLP techniques for dealing with Chinese dimensional ABSA. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.
Anthology ID:
2024.sighan-1.19
Volume:
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Kam-Fai Wong, Min Zhang, Ruifeng Xu, Jing Li, Zhongyu Wei, Lin Gui, Bin Liang, Runcong Zhao
Venues:
SIGHAN | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
165–174
Language:
URL:
https://aclanthology.org/2024.sighan-1.19
DOI:
Bibkey:
Cite (ACL):
Lung-Hao Lee, Liang-Chih Yu, Suge Wang, and Jian Liao. 2024. Overview of the SIGHAN 2024 shared task for Chinese dimensional aspect-based sentiment analysis. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pages 165–174, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Overview of the SIGHAN 2024 shared task for Chinese dimensional aspect-based sentiment analysis (Lee et al., SIGHAN-WS 2024)
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https://preview.aclanthology.org/dois-2013-emnlp/2024.sighan-1.19.pdf
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