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Recognizing Emotion Cause in Conversations
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Given an utterance U, labeled with emotion E, the task is to extract the causal spans S from the conversational history H (including utterance U) that sufficiently represent the causes of emotion E.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations
Towards Interpretable Mental Health Analysis with Large Language Models
PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment
Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations
Discourse-Aware Emotion Cause Extraction in Conversations
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification
Recognizing Emotion Cause in Conversations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.