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Causal Emotion Entailment
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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The Causal Emotion Entailment is a simpler version of the span extraction task. In this task, given a target utterance (U) with emotion E, the goal is to predict which particular utterances in the conversation history H(U) are responsible for the emotion E in the target utterance.
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/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.