  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.
