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Emotion Recognition in Conversation

Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui. .

Papers

Showing 110 of 141 papers

TitleStatusHype
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language ModelsCode3
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
Recent Trends of Multimodal Affective Computing: A Survey from NLP PerspectiveCode2
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion RecognitionCode2
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AICode2
Structure-Aware Transformer for Graph Representation LearningCode2
COSMIC: COmmonSense knowledge for eMotion Identification in ConversationsCode2
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal NuancesCode1
Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion RecognitionCode1
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC TasksCode1
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