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. Experimental results on a benchmark dataset 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。

TSAM: 一种用于因果情感蕴涵的双流注意力模型