Emotion Recognition In Conversation On Meld

평가 지표

Accuracy

Weighted-F1

평가 결과

이 벤치마크에서 각 모델의 성능 결과

			Paper Title
ELR-GNN	68.7	69.9	Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
BiosERC	-	69.83	BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks
CKERC	-	69.27	LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics
InstructERC	-	69.15	InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models
GS-MCC	68.1	69.0	Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
SpeechCueLLM	-	67.604	Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances
Mamba-like Model	68.0	67.6	Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion
TelME	-	67.37	TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation
SPCL-CL-ERC	-	67.25	Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation
EACL	-	67.12	Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
DF-ERC	68.28	67.03	Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition
HiDialog	-	66.96	Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
SACL-LSTM (one seed)	67.89	66.86	Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
FacialMMT	-	66.73	A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations
M2FNet	67.85	66.71	M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation
GraphSmile	67.70	66.71	Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
CFN-ESA	67.85	66.70	CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
SDT	67.55	66.60	A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
CoMPM	-	66.52	CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation
EmotionFlow-large	-	66.50	EmotionFlow: Capture the Dialogue Level Emotion Transitions

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Emotion Recognition In Conversation On Meld

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평가 결과