A Topic-Enhanced Approach for Emotion Distribution Forecasting in Conversations

Xin Lu, Weixiang Zhao, Yanyan Zhao, Bing Qin, Zhentao Zhang, Junjie Wen

Abstract: Emotion Forecasting in Conversations (EFC), the task aims to predict the emotion of next utterance (yet to come), has received more and more attention in recent years. However, this task ignores the one-to-many feature of dialogue and its prediction target is emotion label, which is flawed in most cases. In this work, we propose a new task: Emotion Distribution Forecasting in Conversations (EDFC), which aims to predict the emotion distribution of next utterance. Although this task is more reasonable in real applications, it can only learn using emotion labels in most cases because of the difficulty in obtaining emotion distribution. To address it, we explore the positive role of topic in this task and propose a topic-enhanced approach. Specifically, we first obtain the topic-based emotion distribution prior through topic model and emotion generation model, and then use the emotion distribution prior to enhance original label learning model. To effectively evaluate the distribution prediction results, we construct two datasets for this task, and the experimental results prove the feasibility of the EDFC task as well as the effectiveness of our approach.