Automatically Select Emotion for Response via Personality-affected Emotion Transition

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. 1 .

Personality Enhanced Emotion Generation Modeling for Dialogue Systems

“…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.…”

“…Zhang et al [6] explored how to add personalized and emotionally rich features to conversational systems, employing an unsupervised learning-based approach that correlates user-provided personal information and conversation history with personality traits to enable more personalized and emotionally rich conversational interactions. Wen et al [7] proposed to improve the accuracy of future emotion by combining BigFive personality traits in the VAD emotional space in the context of emotion prediction of texts. Chen et al [8] also constructed an annotated dataset of personality emotions in Chinese for use in an AI dialogue system.…”

CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI

“…However, the BF personality traits of speaker in FriendsPersona change in different conversations, which is contradictory to the personality coherence [53]. PELD [48] is proposed for predicting emotion for response using BF personality traits and VAD vector, in which the personality traits are averaged with personality traits of FriendsPersona [47]. MEmoR [46], a recent multimodal emotion reasoning dataset used for the task of multimodal emotion reasoning, provides a multimodal conversation context, 14 fine-grained emotions and 3 types of personalities (16PF, BF and MBTI).…”

Towards a Sentiment-Aware Conversational Agent

“…[9] or [2]) by adding additional dialogue and textual features, such as the time between interactions, or the emotion of previous sentences, to aid the model. To simulate the emotion transition in humans, the work described in [33] makes use of the Valence-Arousal-Dominance emotion space, which encodes the emotion of words in a 3-dimensional vector space, to calculate the "emotion transition as the variation between the preceding emotion and the response emotion". Predicting the next sentiment can also be part of the text generation model, as described in Section 5.3.…”