Emotion Recognition in Conversations (ERC) has gained increasing attention
for developing empathetic machines. Recently, many approaches have been devoted
to perceiving conversational context by deep learning models. However, these
approaches are insufficient in understanding the context due to lacking the
ability to extract and integrate emotional clues. In this work, we propose
novel Contextual Reasoning Networks (DialogueCRN) to fully understand the
conversational context from a cognitive perspective. Inspired by the Cognitive
Theory of Emotion, we design multi-turn reasoning modules to extract and
integrate emotional clues. The reasoning module iteratively performs an
intuitive retrieving process and a conscious reasoning process, which imitates
human unique cognitive thinking. Extensive experiments on three public
benchmark datasets demonstrate the effectiveness and superiority of the
proposed model.

提出基于情绪认知理论的多轮递推式推理模块和上下文推理网络的设计，用于提取和整合情感线索以充分理解对话上下文，并在三个公共基准数据集上进行广泛实验，证明了模型的有效性和卓越性。