YNU-HPCC at SIGHAN-2024 dimABSA Task: Using PLMs with a Joint Learning Strategy for Dimensional Intensity Prediction
Wangzehui@stu.ynu.edu.cn Wangzehui@stu.ynu.edu.cn, You Zhang, Jin Wang, Dan Xu, Xuejie Zhang
Abstract
The dimensional approach can represent more fine-grained emotional information than discrete affective states. In this paper, a pretrained language model (PLM) with a joint learning strategy is proposed for the SIGHAN-2024 shared task on Chinese dimensional aspect-based sentiment analysis (dimABSA), which requires submitted models to provide fine-grained multi-dimensional (Valance and Arousal) intensity predictions for given aspects of a review. The proposed model consists of three parts: an input layer that concatenates both given aspect terms and input sentences; a Chinese PLM encoder that generates aspect-specific review representation; and separate linear predictors that jointly predict Valence and Arousal sentiment intensities. Moreover, we merge simplified and traditional Chinese training data for data augmentation. Our systems ranked 2nd place out of 5 participants in subtask 1-intensity prediction. The code is publicly available at https://github.com/WZH5127/2024_subtask1_intensity_prediction.- Anthology ID:
- 2024.sighan-1.11
- 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:
- 96–101
- Language:
- URL:
- https://aclanthology.org/2024.sighan-1.11
- DOI:
- Cite (ACL):
- Wangzehui@stu.ynu.edu.cn Wangzehui@stu.ynu.edu.cn, You Zhang, Jin Wang, Dan Xu, and Xuejie Zhang. 2024. YNU-HPCC at SIGHAN-2024 dimABSA Task: Using PLMs with a Joint Learning Strategy for Dimensional Intensity Prediction. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pages 96–101, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at SIGHAN-2024 dimABSA Task: Using PLMs with a Joint Learning Strategy for Dimensional Intensity Prediction (Wangzehui@stu.ynu.edu.cn et al., SIGHAN-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.sighan-1.11.pdf