Learning to Answer Psychological Questionnaire for Personality Detection

Feifan Yang, Tao Yang, Xiaojun Quan, Qinliang Su


Abstract
Existing text-based personality detection research mostly relies on data-driven approaches to implicitly capture personality cues in online posts, lacking the guidance of psychological knowledge. Psychological questionnaire, which contains a series of dedicated questions highly related to personality traits, plays a critical role in self-report personality assessment. We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire. To this end, we propose a new model named Psychological Questionnaire enhanced Network (PQ-Net) to guide personality detection by tracking critical information in texts with a questionnaire. Specifically, PQ-Net contains two streams: a context stream to encode each piece of text into a contextual text representation, and a questionnaire stream to capture relevant information in the contextual text representation to generate potential answer representations for a questionnaire. The potential answer representations are used to enhance the contextual text representation and to benefit personality prediction. Experimental results on two datasets demonstrate the superiority of PQ-Net in capturing useful cues from the posts for personality detection.
Anthology ID:
2021.findings-emnlp.98
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1131–1142
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.98
DOI:
10.18653/v1/2021.findings-emnlp.98
Bibkey:
Cite (ACL):
Feifan Yang, Tao Yang, Xiaojun Quan, and Qinliang Su. 2021. Learning to Answer Psychological Questionnaire for Personality Detection. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1131–1142, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Learning to Answer Psychological Questionnaire for Personality Detection (Yang et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.98.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.98.mp4
Data
PANDORA