Emotion recognition in conversation, which aims to predict the emotion for all utterances, has attracted considerable research attention in recent years. It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels. In this paper, we propose a novel framework which mimics the thinking process when modeling these factors. Specifically, we first comprehend the conversational context with a history-oriented prompt to selectively gather information from predecessors of the target utterance. We then model the speaker's background with an experience-oriented prompt to retrieve the similar utterances from all conversations. We finally differentiate the subtle label semantics with a paraphrasing mechanism to elicit the intrinsic label related knowledge. We conducted extensive experiments on three benchmarks. The empirical results demonstrate the superiority of our proposed framework over the state-of-the-art baselines.

翻译：对话情感识别旨在预测所有话语的情感，近年来受到了大量研究关注。这是一项具有挑战性的任务，因为对话中某句话语的情感识别涉及许多复杂因素，例如对话上下文、说话者背景以及情感标签之间的细微差异。在本文中，我们提出了一种新颖的框架，该框架在建模这些因素时模仿了思考过程。具体来说，我们首先通过面向历史的提示来理解对话上下文，以有选择性地从目标话语的前序中收集信息。然后，我们通过面向经验的提示来建模说话者背景，以从所有对话中检索相似的话语。最后，我们通过改写机制区分细微的标签语义，以引出标签本身固有的相关知识。我们在三个基准上进行了广泛的实验。实验结果证明了我们提出的框架相对于现有最先进基线的优越性。