Humans convey emotions through daily dialogues, making emotion understanding
a crucial step of affective intelligence. To understand emotions in dialogues,
machines are asked to recognize the emotion for an utterance (Emotion
Recognition in Dialogues, ERD); based on the emotion, then find causal
utterances for the emotion (Emotion Cause Extraction in Dialogues, ECED). The
setting of the two tasks requires first ERD and then ECED, ignoring the mutual
complement between emotion and cause. To fix this, some new tasks are proposed
to extract them simultaneously. Although the current research on these tasks
has excellent achievements, simply identifying emotion-related factors by
classification modeling lacks realizing the specific thinking process of causes
stimulating the emotion in an explainable way. This thinking process especially
reflected in the reasoning ability of Large Language Models (LLMs) is
under-explored. To this end, we propose a new task "Emotion Deducing
Explanation in Dialogues" (EDEN). EDEN recognizes emotion and causes in an
explicitly thinking way. That is, models need to generate an explanation text,
which first summarizes the causes; analyzes the inner activities of the
speakers triggered by the causes using common sense; then guesses the emotion
accordingly. To support the study of EDEN, based on the existing resources in
ECED, we construct two EDEN datasets by human effort. We further evaluate
different models on EDEN and find that LLMs are more competent than
conventional PLMs. Besides, EDEN can help LLMs achieve better recognition of
emotions and causes, which explores a new research direction of explainable
emotion understanding in dialogues.

在日常对话中，人们通过表达情感来传递情感信息，因此情感理解成为情感智能的关键一步。为了理解对话中的情感，我们需要让机器能够识别话语中的情感（对话情感识别，ERD），然后根据情感找出导致该情感的话语（对话情感原因提取，ECED）。目前的研究忽略了情感和原因之间的相互补充关系，因此我们提出了一种新的任务，即 “对话情感推导解释”（EDEN）。EDEN 以明确的推理方式识别情感和原因，模型需要生成一个解释文本，对导致情感的原因进行总结，使用常识分析讲话者的内部活动，然后根据推导出的原因猜测情感。为了支持 EDEN 的研究，我们基于 ECED 中的现有资源，通过人力努力构建了两个 EDEN 数据集。我们在 EDEN 上评估了不同模型，并发现大型语言模型（LLMs）比传统的预训练语言模型（PLMs）更有竞争力。此外，EDEN 可以帮助 LLMs 更好地识别情感和原因，探索了对话中可解释的情感理解的新研究方向。