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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

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Showing 126–141 of 141 papers

TitleStatusHype
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement—0
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations—0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
Emotion Dynamics Modeling via BERT—0
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition—0
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition—0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition—0
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog—0
Hierarchical Transformer Network for Utterance-level Emotion Recognition—0
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations—0
Hybrid Curriculum Learning for Emotion Recognition in Conversation—0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations—0
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