  Understanding the process of emotion generation is crucial for analyzing the
causes behind emotions. Causal Emotion Entailment (CEE), an
emotion-understanding task, aims to identify the causal utterances in a
conversation that stimulate the emotions expressed in a target utterance.
However, current works in CEE mainly focus on modeling semantic and emotional
interactions in conversations, neglecting the exploration of the
emotion-generation process. This hinders the models from deeply understanding
emotions, restricting their ability to produce explainable predictions. In this
work, inspired by the emotion generation process of
"stimulus-appraisal-emotion" in the cognitive appraisal theory, we introduce a
step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to
infer the stimulus from the target emotional expressions in conversations.
Specifically, we first introduce the ECR-Chain to ChatGPT via few-shot
prompting, which significantly improves its performance on the CEE task. We
further propose an automated construction process to utilize ChatGPT in
building an ECR-Chain set, which can enhance the reasoning abilities of smaller
models through supervised training and assist the Vicuna-7B model in achieving
state-of-the-art CEE performance. Moreover, our methods can enable these
generative language models to effectively perform emotion-cause reasoning in an
explainable manner. Our code, data and more details are at
https://github.com/hzp3517/ECR-Chain.
