对话系统中受个性影响的情感生成

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun
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引用次数: 0

摘要

对话系统要想在各种应用场景中提供类似人类的交互,就必须为回应生成适当的情感。以前的大多数对话系统都试图通过从匿名对话数据中学习移情礼仪来实现这一目标。然而,这些方法产生的情绪反应可能会不一致,从而降低用户参与度和服务质量。心理学研究结果表明,人类的情感表达源于个性特征。因此,我们提出了一项新任务--"受个性影响的情绪生成",根据对话系统的个性生成情绪,并通过受个性影响的情绪转换进一步研究解决方案。具体来说,我们首先构建了一个包含情感和个性注释的日常对话数据集--个性情感线数据集(PELD)。随后,我们分析了这一任务所面临的挑战,即:(1)异构整合个性和情感因素;(2)提取对话语境中的多粒度情感信息。最后,我们提出通过模拟对话系统中的情绪转换过程,将个性作为转换权重建模,从而解决上述难题。我们在 PELD 上进行了广泛的实验评估。结果表明,采用我们的方法,在宏 F1 和加权 F1 中,情感生成性能分别比基于 BERT 的模型提高了 13% 和 5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personality-affected Emotion Generation in Dialog Systems

Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialog dataset, Personality EmotionLines Dataset (PELD), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialog system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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