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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.98.pdf
- Data
- PANDORA