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Emotion Detection on MELD (test)

0.699Weighted-F1ELR-GCN
0.4891280.5436140.59810.652586Sep 24, 2019Jul 9, 2020Apr 25, 2021Feb 9, 2022Nov 25, 2022Sep 11, 2023Jun 27, 2024

Evaluation results

MethodLinks
ELR-GCN2024.060.699—0.8020.8360.3680.3540.1920.1310.8020.8360.7650.6970.5560.130.5210.5770.687——
TelME2024.010.6737—0.8022—0.6033—0.2697—0.4345—0.6567—0.2642—0.567————
M2FNet2024.010.6671——————————————————
FacialMMT2024.010.6658—0.8013—0.5963—0.1918—0.4199—0.6488—0.1818—0.56————
DER-GCN2024.060.661—0.7680.8060.5050.510.1480.1040.5670.4150.6930.6430.1720.1030.5250.5740.668——
LR-GCN2024.060.656—0.7670.80.5330.552000.4960.3510.680.6440.1070.0270.480.510.657——
UniMSE2024.010.6551——————————————————
COMPM2024.060.653—0.7830.820.4830.4920.0170.0290.3590.3230.7140.6150.0310.0280.4220.4580.641——
EmoBERTa2024.060.652—0.7890.8250.5020.5020.0180.0190.3330.3120.7210.6170.0310.0250.4330.4640.641——
EmoCaps2024.010.64—0.7712—0.6319—0.0303—0.4252—0.575—0.0769—0.5754————
DAG-ERC2024.010.6365——————————————————
DialogueTRM2024.010.635——————————————————
RGAT2024.060.611—0.760.7810.4010.4150.030.0240.3210.3070.6810.5860.0450.0220.40.4460.603——
A-DMN2024.060.604—0.7650.7890.5620.5530.0820.0860.2210.2490.5980.5740.0120.0340.4130.4090.615——
MM-DFN2024.010.5946—0.7776—0.5069———0.2294—0.5478———0.4782————
ConGCN2024.010.594—0.767—0.503—0.087—0.285—0.531—0.106—0.468————
GA2MIF2024.010.5894—0.7692—0.4908———0.2718—0.5187———0.4852————
ConGCN2024.060.587—0.4680.4540.1060.0880.0870.0810.5310.5460.7670.7520.2850.2630.5030.4840.594——
MMGCN2024.010.5865——————————————————
KET2019.090.5818——————————————————
DialogueRNN2024.010.5703—0.735—0.494—0.012—0.238—0.507—0.017—0.415————
CNN+cLSTM2019.090.5687——————————————————
KET_StdAttn2019.090.5682——————————————————
DialogueRNNSpeaker Selection=6 main + 1 universal2019.090.5627——————————————————
KET_SingleSelfAttn2019.090.5624——————————————————
BERT_BASE2019.090.5621——————————————————
bc-LSTM2024.060.559—0.7840.7380.4680.4770.0380.0540.2240.2510.5160.5130.0430.0520.3670.3840.575——
DialogueRNN2024.060.559—0.7210.7350.5440.4940.0160.0120.2390.2380.520.5070.0150.0170.410.4150.561——
CNN2019.090.5586——————————————————
TextCNN2024.060.55—0.7620.7490.4330.4550.0460.0370.1820.2110.4610.4940.0890.0830.3530.3450.563——
DialogueGCN2024.060.547—0.7030.7210.4240.4170.030.0280.2090.2180.4470.4420.0650.0670.390.3650.549——
cLSTM2019.090.4972——————————————————
BC-LSTMcontextual information=true, speaker information=false2020.10———0.76—0.48—0—0.16—0.5—0—0.46———
BERT2023.04——————————————————0.6131
BiF-AGRUContext=Bidirectional, Gated Memory=Attention Gated RU2019.11—0.38581.60.75550.50.4980.10422.30.26951.70.51713.70.17234.50.38659.50.575—
BiF-BiAGRUContext=Bidirectional, Gated Memory=Bidirectional Attention Gated RU2019.11—0.37580.70.75450.70.4797.20.120.90.26353.90.52110.40.12833.90.38159.20.571—
CGCNcontextual information=true, speaker information=true2020.10———0.77—0.5—0.09—0.28—0.53—0.11—0.47———
CNN2019.11—0.339—0.749—0.455—0.037—0.211—0.494—0.083—0.345—0.55—
DialogueRNNDirectionality=Unidirectional2019.11—0.34572.10.73554.40.4941.60.01223.90.238520.5071.50.01741.90.41556.10.559—
DRNNcontextual information=true, speaker information=false2020.10———0.73—0.52—0—0.25—0.53—0—0.46———
G-MFNcontextual information=false, speaker information=false2020.10———0.76—0.41—0—0.13—0.47—0—0.4———
HiDialogencoder=RoBERTa2023.04——————————————————0.6696
MATcontextual information=false, speaker information=false2020.10———0.63—0.44—0.09—0.22—0.42—0.15—0.27———
MNTcontextual information=false, speaker information=false2020.10———0.66—0.46—0.12—0.24—0.41—0.21—0.27———
NTcontextual information=false, speaker information=false2020.10———0.54—0.41—0.05—0.21—0.39—0.11—0.25———
RoBERTaencoder=RoBERTa2023.04——————————————————0.6419
scLSTMDirectionality=Unidirectional2019.11—0.35378.40.73846.80.4773.80.05422.40.25151.60.5134.30.05236.70.38457.50.559—
TUCORE-GCN2023.04——————————————————0.6536
UniF-AGRUContext=Unidirectional, Gated Memory=Attention Gated RU2019.11—0.3880.30.75153.70.4919.80.10619.70.25550.50.511140.16433.90.38258.80.57—
UniF-BiAGRUContext=Unidirectional, Gated Memory=Bidirectional Attention Gated RU2019.11—0.38683.40.76449.10.4979.20.11521.60.2752.40.52412.20.1434.90.39460.30.581—