Psycholinguistic Tripartite Graph Network for Personality Detection

Tao Yang, Feifan Yang, Haolan Ouyang, Xiaojun Quan


Abstract
Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one’s language use and his psychological traits. In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. The graph network injects structural psycholinguistic knowledge in LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The initializer is employed to provide initial embeddings for the graph nodes. To reduce the computational cost in graph learning, we further propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties in the tripartite graph. Benefiting from the tripartite graph, TrigNet can aggregate post information from a psychological perspective, which is a novel way of exploiting domain knowledge. Extensive experiments on two datasets show that TrigNet outperforms the existing state-of-art model by 3.47 and 2.10 points in average F1. Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%, respectively, in comparison to the original GAT in our setting.
Anthology ID:
2021.acl-long.326
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4229–4239
Language:
URL:
https://aclanthology.org/2021.acl-long.326
DOI:
10.18653/v1/2021.acl-long.326
Bibkey:
Cite (ACL):
Tao Yang, Feifan Yang, Haolan Ouyang, and Xiaojun Quan. 2021. Psycholinguistic Tripartite Graph Network for Personality Detection. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4229–4239, Online. Association for Computational Linguistics.
Cite (Informal):
Psycholinguistic Tripartite Graph Network for Personality Detection (Yang et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.326.pdf
Video:
 https://aclanthology.org/2021.acl-long.326.mp4
Data
PANDORA