Papers with Code 아카이브
Recognizing Emotion Cause in Conversations
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 2
- 보존된 평가 기록
- 8
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bladedancer957/tsam
공식 코드TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
https://github.com/circle-hit/kbcin
공식 코드Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment
https://github.com/declare-lab/RECCON
공식 코드https://github.com/exploration-lab/mutec
공식 코드Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations
https://github.com/hzp3517/ecr-chain
공식 코드https://github.com/leqsnan/kec
공식 코드https://github.com/skywalker023/focused-empathy
공식 코드Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
https://github.com/stevekgyang/mentalllama
공식 코드Towards Interpretable Mental Health Analysis with Large Language Models
https://github.com/xiaojiegu/page
공식 코드PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
https://github.com/MindSpore-scientific/code-1/tree/main/TSA_mindspore
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
https://github.com/pwc-1/Paper-9/tree/main/7/TSA_mindspore
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
https://github.com/slptongji/MPEG
https://github.com/stevekgyang/mentallama
Towards Interpretable Mental Health Analysis with Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.