Interactive double states emotion cell model for textual dialogue emotion prediction
Interactive double states emotion cell model for textual dialogue emotion prediction
复制标题
用于文本对话情感预测的交互式双态情感细胞模型
DOI：
10.1016/j.knosys.2019.105084
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发表时间：
2020-02
影响因子：
8.8
通讯作者：
Suge Wang
中科院分区：
文献类型：
--
作者：
Dayu Li;Yang Li;Suge Wang
日常对话中充满了情绪，这些情绪控制着对话的走向，影响着对话者对彼此的态度，了解对话中的人类情绪对情感抚慰、人机交互和智能问答都具有重要意义。本文定义了文本对话中的情感预测任务。与文本情感识别任务从对话者的话语中得出对话者当前的情绪状态不同，情感预测任务旨在在对话者说话之前预测对话者未来的情绪状态。此外，本文还总结并解释了文本对话中情感传播的三个显著特征：语境依赖性、持久性和传染性。考虑到这些特点，提出了一种完全数据驱动的交互式双状态情感细胞模型（IDS-ECM）。该模型有两层。第一层自动提取历史对话的情感信息，用于描述文本对话情感的语境依赖性。第二层模拟了对话者在对话过程中情绪状态的变化过程，描绘了情绪的持续性和传染性。在两个人工标注数据集上的实验结果表明，所提模型在宏观平均F1评价指标上优于基线，能够模拟对话过程中的情绪变化，从而对情绪进行高精度预测。实验结果也揭示了对话中不同情绪类别之间的交流差异，对今后的研究具有指导意义。
Daily dialogues are full of emotions that control the trends of dialogues and influence the attitudes of interlocutors toward each other, and understanding the human emotions in dialogues is of great significance in emotional comfort, human–computer interaction and intelligent question-answering. This paper defines a new task called emotion prediction in textual dialogue. Different from the text emotion recognition task, which derives the current emotional state of interlocutor from the utterance, emotion prediction aims at predicting the future emotional state of interlocutor before the interlocutor utters something. Moreover, this paper summarizes and explains three notable characteristics of emotional propagation in text dialogue: context dependence, persistence and contagiousness. By considering these characteristics, a fully data-driven interactive double states emotion cell model (IDS-ECM) is proposed. The model has two layers. The first layer automatically extracts the emotional information of historical dialogue and is used to describe the contextual dependence of the textual dialogue emotion. The second layer models the change process of interlocutors’ emotional states during the dialogue and depicts the persistence and contagiousness of emotions. Experimental results on two manually annotated datasets show that the proposed model is superior to the baseline in the macro-averaged F1 evaluation metric and that the proposed model can simulate the emotional changes in the process of dialogue so as to predict the emotions with high accuracy. The experimental results also reveal the communication differences between different emotional categories in dialogue, which is of guiding significance for future research.
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DOI：
10.1016/j.specom.2011.01.011
发表时间：
2011-11
期刊：
Speech Commun.
影响因子：
--
作者：
Björn Schuller;A. Batliner;S. Steidl;Dino Seppi
通讯作者：
Björn Schuller;A. Batliner;S. Steidl;Dino Seppi
DOI：
10.1016/b978-0-12-386980-7.50023-x
发表时间：
2013
期刊：
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影响因子：
--
作者：
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通讯作者：
J. Leeuwen
DOI：
10.24963/ijcai.2018/643
发表时间：
2018-07
期刊：
--
影响因子：
--
作者：
Hao Zhou;Tom Young;Minlie Huang;Haizhou Zhao;Jingfang Xu;Xiaoyan Zhu
通讯作者：
Hao Zhou;Tom Young;Minlie Huang;Haizhou Zhao;Jingfang Xu;Xiaoyan Zhu
DOI：
10.18653/v1/n18-1193
发表时间：
2018-06-01
期刊：
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting
影响因子：
--
作者：
Hazarika, Devamanyu;Poria, Soujanya;Zimmermann, Roger
通讯作者：
Zimmermann, Roger
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
Orestes Appel;Hamido Fujita;Orestes Appel;F. Chiclana;Jenny Carter
通讯作者：
Orestes Appel;Hamido Fujita;Orestes Appel;F. Chiclana;Jenny Carter