Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation. The order of utterances in the conversation affects the causal inference. However, most current position encoding strategies ignore the order relation among utterances and speakers. To address the issue, we devise a novel position-aware graph to encode the entire conversation, fully modeling causal relations among utterances. The comprehensive experiments show that our method consistently achieves state-of-the-art performance on two challenging test sets, proving the effectiveness of our model. Our source code is available on Github: https://github.com/XiaojieGu/PAGE.

翻译：对话因果情感蕴含（C2E2）是一项旨在识别对话中目标情感对应原因的任务。对话中话语的次序影响因果推理，然而，现有的大多数位置编码策略忽略了话语与说话者之间的顺序关系。为解决该问题，我们设计了一种新颖的位置感知图来编码整个对话，充分建模话语间的因果关系。综合实验表明，我们的方法在两个具有挑战性的测试集上持续实现了最优性能，证明了模型的有效性。我们的源代码已在Github上公开：https://github.com/XiaojieGu/PAGE。