PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
SeqPE: Transformer with Sequential Position Encoding
We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
토론
이 논문과 연결된 포럼 글이 아직 없습니다.