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Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks

arXiv 2401.05871 ↗First posted 11 Jan 2024cs.CLcited by 0

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Abstract (arXiv)

Personality recognition is useful for enhancing robots' ability to tailor user-adaptive responses, thus fostering rich human-robot interactions. One of the challenges in this task is a limited number of speakers in existing dialogue corpora, which hampers the development of robust, speaker-independent personality recognition models. Additionally, accurately modeling both the interdependencies among interlocutors and the intra-dependencies within the speaker in dialogues remains a significant issue. To address the first challenge, we introduce personality trait interpolation for speaker data augmentation. For the second, we propose heterogeneous conversational graph networks to independently capture both contextual influences and inherent personality traits. Evaluations on the RealPersonaChat corpus demonstrate our method's significant improvements over existing baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 11 Jan 2024.

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References · 20

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8 more
  1. 2019Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddingscited by 31
  2. 2022CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AIcited by 25
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