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Personality prediction from task-oriented and open-domain human-machine dialogues.

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  • 1Graduate School of Informatics, Nagoya University, Nagoya, Japan. guo.ao.i6@f.mail.nagoya-u.ac.jp.

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Dialogue systems can predict user personality traits from conversations. Open-domain dialogue systems show better personality prediction accuracy than task-oriented systems, though Extraversion is predictable in both.

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Area of Science:

  • Natural Language Processing
  • Human-Computer Interaction
  • Computational Linguistics

Background:

  • Dialogue systems aim to adapt to user personalities for improved interaction.
  • Previous research explored personality prediction using MBTI traits in end-to-end systems.
  • The generalizability of personality prediction across different dialogue systems and traits remains unclear.

Purpose of the Study:

  • To investigate the feasibility of personality trait prediction from dialogue across various system types and personality frameworks.
  • To compare the effectiveness of open-domain versus task-oriented dialogue systems for personality prediction.
  • To analyze the influence of dialogue characteristics on prediction accuracy.

Main Methods:

  • Recruited 378 participants for personality questionnaires and task-oriented dialogues (pipeline and end-to-end systems).
  • Engaged 186 additional participants in open-domain dialogue systems.
  • Developed BERT-based models to predict personality traits from collected dialogue data.

Main Results:

  • Open-domain dialogue systems yielded higher overall personality prediction accuracy compared to task-oriented systems.
  • Extraversion (a Big Five trait) was equally predictable in open-domain and pipeline task-oriented dialogues.
  • Models trained on task-oriented dialogue data could not predict traits from open-domain dialogue, and vice versa.

Conclusions:

  • Dialogue system type significantly impacts personality prediction accuracy.
  • Open-domain dialogues offer a more promising avenue for broad personality trait prediction.
  • Future research should consider dialogue context, system type, and specific personality traits for effective prediction.