PAED: Zero-Shot Persona Attribute Extraction in Dialogues

Zhu, Luyao; Li, Wei; Mao, Rui; Pandelea, Vlad; Wang, Zhaoxia

doi:10.18653/v1/2023.acl-long.544

Cited by 17 publications

(1 citation statement)

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“…Johns et al [18] are also interacting robots with predefined personalities to influence emotional state transfer. Zhu et al [19] propose a comparative learning and generation-based model for zero-shot personality attribute extraction to facilitate HCI research under personality. Wen et al [7] constructed a dataset with personality and emotion annotations and designed an emotion prediction model to go through the conversation to predict emotion in future moments.…”

Section: Integrating Personality Emotion Modeling In Dialogue Systemsmentioning

confidence: 99%

Personality Enhanced Emotion Generation Modeling for Dialogue Systems

Ma,

Jia,

Zhou

et al. 2023

Cogn Comput

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Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user’s unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

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Section: Integrating Personality Emotion Modeling In Dialogue Systemsmentioning

confidence: 99%