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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 51–75 of 141 papers

TitleStatusHype
Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression RecognitionCode1
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in ConversationCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological KnowledgeCode1
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition—0
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations—0
Emotion Dynamics Modeling via BERT—0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations—0
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations—0
CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation—0
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation—0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation—0
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation—0
ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy—0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation—0
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation—0
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
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