Emotion detection is a critical technology extensively employed in diverse fields. While the incorporation of commonsense knowledge has proven beneficial for existing emotion detection methods, dialogue-based emotion detection encounters numerous difficulties and challenges due to human agency and the variability of dialogue content.In dialogues, human emotions tend to accumulate in bursts. However, they are often implicitly expressed. This implies that many genuine emotions remain concealed within a plethora of unrelated words and dialogues.In this paper, we propose a Dynamic Causal Disentanglement Model based on hidden variable separation, which is founded on the separation of hidden variables. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions, thereby enabling more precise emotion recognition. First, we introduce a novel Causal Directed Acyclic Graph (DAG) to establish the correlation between hidden emotional information and other observed elements. Subsequently, our approach utilizes pre-extracted personal attributes and utterance topics as guiding factors for the distribution of hidden variables, aiming to separate irrelevant ones. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables, enabling the accumulation of emotion-related information throughout the conversation. To guide this disentanglement process, we leverage the ChatGPT-4.0 and LSTM networks to extract utterance topics and personal attributes as observed information.Finally, we test our approach on two popular datasets in dialogue emotion detection and relevant experimental results verified the model's superiority.

翻译：情感检测是一项广泛应用于不同领域的关键技术。尽管常识知识的融入对现有情感检测方法有所助益，但由于人的主观能动性和对话内容的变异性，基于对话的情感检测仍面临诸多困难与挑战。在对话中，人类情感倾向于突发式积累，却往往以隐晦方式表达。这意味着许多真实情感被隐藏在海量无关词句与对话之中。本文提出一种基于隐变量分离的动态因果解耦模型，该模型有效分解对话内容，并探究情感的时间累积特性，从而实现更精准的情感识别。首先，我们引入一种新颖的因果有向无环图( DAG )来建立隐藏情感信息与其他观测元素之间的关联。其次，本方法利用预提取的个人属性与话语主题作为隐变量分布的引导因素，旨在分离无关变量。具体而言，我们提出一种动态时间解耦模型来推断话语与隐变量的传播过程，使得情感相关信息在对话过程中得以累积。为引导这一解耦过程，我们利用ChatGPT-4.0和LSTM网络提取话语主题与个人属性作为观测信息。最后，我们在两个流行的对话情感检测数据集上测试了本方法，相关实验结果验证了该模型的优越性。