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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 1120 of 141 papers

TitleStatusHype
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
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation0
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC TasksCode1
MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting0
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations0
FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversationsCode0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning0
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum0
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