Automatically Select Emotion for Response via Personality-affected Emotion Transition

TL;DR

This paper introduces a dialog system that automatically selects appropriate response emotions by modeling emotion transitions influenced by personality traits, leading to more consistent and human-like emotional interactions.

Contribution

It proposes a novel personality-affected emotion transition model for dialog systems, integrating emotion dynamics and personality traits to improve emotional response consistency.

Findings

Effective emotion transition modeling validated by experiments

Improved emotional consistency in dialog responses

Constructed a new dataset with emotion and personality labels

Abstract

To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked. This may lead to inconsistent emotional expressions and disinterest users. To tackle this issue, we propose to equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion transition of humans in conversation. In detail, the emotion of the dialog system is transitioned from its preceding emotion in context. The transition is triggered by the preceding dialog context and affected by the specified personality trait. To achieve this, we first model the emotion transition in the dialog system as the variation between the preceding emotion and the response emotion in the Valence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks to encode the preceding dialog context and the specified personality traits to compose the variation. Finally, the emotion for response is selected from the sum of the preceding emotion and the variation. We construct a dialog dataset with emotion and personality labels and conduct emotion prediction tasks for evaluation. Experimental results validate the effectiveness of the personality-affected emotion transition.