Prompt-based Pre-trained Model for Personality and Interpersonal Reactivity Prediction

Bin Li, Yixuan Weng, Qiya Song, Fuyan Ma, Bin Sun, Shutao Li


Abstract
This paper describes the LingJing team’s method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI). In this paper, we adopt the prompt-based method with the pre-trained language model to accomplish these tasks. Specifically, the prompt is designed to provide knowledge of the extra personalized information for enhancing the pre-trained model. Data augmentation and model ensemble are adopted for obtaining better results. Extensive experiments are performed, which shows the effectiveness of the proposed method. On the final submission, our system achieves a Pearson Correlation Coefficient of 0.2301 and 0.2546 on Track 3 and Track 4 respectively. We ranked 1-st on both sub-tasks.
Anthology ID:
2022.wassa-1.28
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
265–270
Language:
URL:
https://aclanthology.org/2022.wassa-1.28
DOI:
10.18653/v1/2022.wassa-1.28
Bibkey:
Cite (ACL):
Bin Li, Yixuan Weng, Qiya Song, Fuyan Ma, Bin Sun, and Shutao Li. 2022. Prompt-based Pre-trained Model for Personality and Interpersonal Reactivity Prediction. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 265–270, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Prompt-based Pre-trained Model for Personality and Interpersonal Reactivity Prediction (Li et al., WASSA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.28.pdf