This paper tackles the task of emotion-cause pair extraction in the unsupervised domain adaptation setting. The problem is challenging as the distributions of the events causing emotions in target domains are dramatically different than those in source domains, despite the distributions of emotional expressions between domains are overlapped. Inspired by causal discovery, we propose a novel deep latent model in the variational autoencoder (VAE) framework, which not only captures the underlying latent structures of data but also utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains. To facilitate knowledge transfer across domains, we also propose a novel variational posterior regularization technique to disentangle the latent representations of emotions from those of events in order to mitigate the damage caused by the spurious correlations related to the events in source domains. Through extensive experiments, we demonstrate that our model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on a English benchmark in terms of weighted-average F1 score. The source code will be publicly available upon acceptance.

本文提出了一个基于深度潜变量模型和变分自编码器（VAE）框架的新方法，用于在无监督域适应设置中提取情感-原因对。通过利用情感作为桥梁，将不同域中事件的分布联系起来，并采用变分后验正则化技术来降低源域中与事件相关的虚假相关性，从而促进跨域知识传递。通过广泛的实验证明，在中文和英文基准测试中，我们的模型的加权平均F1分数分别比最强基线提高了11.05%和2.45%。源代码将在接受后公开提供。

基于因果发现的无监督领域自适应情感因果对提取