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
this https URL

通过逐步推理方法 (ECR-Chain) 以及促进模型优化的 few-shot prompting，本研究在生成式语言模型中引入情感生成过程的因果关系，并且能够在可解释的方式下成功进行情感因果推理。

ECR-Chain: 通过推理链推进生成式语言模型以提高情感原因推理

ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause  Reasoners through Reasoning Chains

Causal Emotion Entailment aims to identify causal utterances that are
responsible for the target utterance with a non-neutral emotion in
conversations. Previous works are limited in thorough understanding of the
conversational context and accurate reasoning of the emotion cause. To this
end, we propose Knowledge-Bridged Causal Interaction Network (KBCIN) with
commonsense knowledge (CSK) leveraged as three bridges. Specifically, we
construct a conversational graph for each conversation and leverage the
event-centered CSK as the semantics-level bridge (S-bridge) to capture the deep
inter-utterance dependencies in the conversational context via the CSK-Enhanced
Graph Attention module. Moreover, social-interaction CSK serves as
emotion-level bridge (E-bridge) and action-level bridge (A-bridge) to connect
candidate utterances with the target one, which provides explicit causal clues
for the Emotional Interaction module and Actional Interaction module to reason
the target emotion. Experimental results show that our model achieves better
performance over most baseline models. Our source code is publicly available at
this https URL.

本论文提出了利用常识知识作为桥梁的 Causal Emotion Entailment 方法，其中知识被用作三个桥梁，通过增强的图注意模块提取深层次的对话上下文信息，以及情感和行为相关的常识知识作为情感和动作相关的桥梁，使模型在情感识别方面呈现出了更好的性能。

因果情感蕴涵的知识桥接因果交互网络

Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment

Causal Emotion Entailment (CEE) aims to discover the potential causes behind
an emotion in a conversational utterance. Previous works formalize CEE as
independent utterance pair classification problems, with emotion and speaker
information neglected. From a new perspective, this paper considers CEE in a
joint framework. We classify multiple utterances synchronously to capture the
correlations between utterances in a global view and propose a Two-Stream
Attention Model (TSAM) to effectively model the speaker's emotional influences
in the conversational history. Specifically, the TSAM comprises three modules:
Emotion Attention Network (EAN), Speaker Attention Network (SAN), and
interaction module. The EAN and SAN incorporate emotion and speaker information
in parallel, and the subsequent interaction module effectively interchanges
relevant information between the EAN and SAN via a mutual BiAffine
transformation. Extensive experimental results demonstrate that our model
achieves new State-Of-The-Art (SOTA) performance and outperforms baselines
remarkably.

本文提出了一种基于多个话语的联合分类方法，使用两个注意力网络与相互交互的模块来捕捉说话人的情感影响，从而实现对话历史中情感原因的发现，实验结果表明该模型具有最先进的性能并优于基线模型。