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:C2E2是一项旨在确认对话中目标情感对应的原因的任务。 谈话中的言词顺序会影响因果推断。 然而, 多数当前的位置编码战略忽略了言词和发言者之间的顺序关系。 为了解决这个问题, 我们设计了一个新颖的立场认知图, 用于编码整个对话, 充分模拟言词之间的因果关系。 全面实验显示, 我们的方法始终在两个挑战性测试集上取得最新业绩, 证明了我们模式的有效性。 我们的来源代码可以在 Github: https://github.com/XiaojieGu/PAGE上查阅 。</s>