Emotion Recognition In Conversation On

평가 지표

Accuracy
Weighted-F1

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Accuracy
Weighted-F1
Paper TitleRepository
FATRER69.6969.35FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition-
SpeechCueLLM-72.596Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances-
GA2MIF69.7570.00GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection-
M2FNet69.6969.86M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation-
DialogXL66.366.2DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition-
KI-Net-67.00Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations-
AccumWR-67.65Accumulating Word Representations in Multi-level Context Integration for ERC Task
EACL-70.41Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation-
Pretrained Hierarchical Transformer66.0565.37Hierarchical Pre-training for Sequence Labelling in Spoken Dialog-
SKAIG-ERC-66.98Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge-
DF-ERC71.8471.75Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition-
S+PAGE-68.72S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation-
BiERU-lc-65.22BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis-
DialogueGCN-64.37DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation-
SACL-LSTM (one seed)69.6269.70Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations-
GraphCFC69.1368.91GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition-
CKERC-72.40LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics-
VHRED-59.56Conversational Transfer Learning for Emotion Recognition-
SumAggGIN-66.96Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition-
Attention-BLSTM65.962.9Integrating Recurrence Dynamics for Speech Emotion Recognition-
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Emotion Recognition In Conversation On | SOTA | HyperAI초신경