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

Autor:	Fu, Yahui, Song, Haiyue, Zhao, Tianyu, Kawahara, Tatsuya
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: This paper has been accepted for presentation at International Workshop on Spoken Dialogue Systems Technology 2024 (IWSDS 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.05871 Zobrazit plný text záznamu View this record from Arxiv