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.

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

RECCON

RECCON is a dataset for the task of recognizing emotion cause in conversations. Source: Recognizing Emotion Cause in Conversations

EmoCause is a dataset of annotated emotion cause words in emotional situations from the EmpatheticDialogues valid and test set. The goal is to recognize emotion cause words in sentences by training only on sentence-level emotion labels without word-level labels (i.e., weakly-supervised emotion cause recognition). EmoCause is based on the fact that humans do not recognize the cause of emotions with supervised learning on word-level cause labels. Thus, we do not provide a training set. * Number of emotion categories: 32 * Average number of cause words per utterance: 2.3 * Total number of utterances: 4.6K (valid: 3.8K / test: 0.8K)

CANDOR Corpus

The CANDOR corpus is a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.