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.

本研究旨在为对话系统引入人格特质，通过模拟人类在对话中的情感转换来自动选择回应中的情感，使用 VAD 情感空间和神经网络模型来呈现情感二元组之间的转换，并验证了此方法的有效性。