基于深度学习的对话情绪生成研究综述

doi:10.3778/j.issn.1002-8331.2304-0131

摘要/Abstract

摘要： 情绪生成是人工情感计算研究中的子任务，在对话系统中情绪生成任务旨在生成待回复话语中的情绪类别。对话情绪生成可以推动对话情绪理解和对话表达研究，同时在智能闲聊机器人、情绪安慰、推荐系统和人机情感交互等诸多智能化领域具有重要的理论意义和实际应用价值。得益于深度神经网络在自然语言处理领域的优异表现，基于深度学习的对话系统情绪生成受到越来越多研究人员的关注。总结目前基于深度学习的对话情绪生成相关工作，现阶段利用深度学习的对话系统情绪生成相关研究主要包含三方面内容：情绪感知、情绪预测和情绪决策。简要介绍了一些常用的情绪对话数据集，最后对该任务当前问题进行了归纳概况并展望未来发展趋势。

关键词: 对话情绪生成, 人工情感, 深度学习

Abstract: Emotion generation is a subtask in the study of artificial affective computing, where the emotion generation task in a dialog system aims to generate emotion categories in the discourse to be replied to. Conversational emotion generation aims to generate emotion categories in the discourse to be replied, which can promote the research of conversational emotion understanding and conversational expression, and also has important theoretical significance and practical application value in many intelligent fields such as intelligent gossip bots, emotional comfort, recommendation systems and human-computer emotional interaction. Thanks to the excellent performance of deep neural networks in the field of natural language processing, deep learning-based emotion generation for conversational systems has received more and more attention from researchers. To summarize the current work related to deep learning-based conversational emotion generation, the current research on emotion generation for conversational systems using deep learning mainly contains three aspects：emotion perception, emotion prediction and emotion decision making. Some commonly used emotion dialogue datasets are briefly introduced, and finally, an overview of current issues in this task is summarized and future trends are prospected.

Key words: emotion generation in conversation, artificial emotions, deep learning

周钰童, 马志强, 许璧麒, 贾文超, 吕凯, 刘佳. 基于深度学习的对话情绪生成研究综述[J]. 计算机工程与应用, 2024, 60(7): 13-25.

ZHOU Yutong, MA Zhiqiang, XU Biqi, JIA Wenchao, LYU Kai, LIU Jia. Survey of Deep Learning-Based on Emotion Generation in Conversation[J]. Computer Engineering and Applications, 2024, 60(7): 13-25.

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