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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
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
Conversational Transfer Learning for Emotion Recognition—0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations—0
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances—0
Emotion Dynamics Modeling via BERT—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition—0
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition—0
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations—0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition—0
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog—0
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