Automatic Text-Based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings (Student Abstract)

Authors

  • Hang Jiang Stanford University
  • Xianzhe Zhang Stanford Univsersity
  • Jinho D. Choi Emory University

DOI:

https://doi.org/10.1609/aaai.v34i10.7182

Abstract

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona , by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.

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Published

2020-04-03

How to Cite

Jiang, H., Zhang, X., & Choi, J. D. (2020). Automatic Text-Based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13821-13822. https://doi.org/10.1609/aaai.v34i10.7182

Issue

Section

Student Abstract Track