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Models/BERT-GCN

BERT-GCN

Reported on 9 benchmarks across 3 tasks · 1 paper · 9 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing6 results

Text ClassificationonSemEval 2018 Task 1E-c
Accuracy· 2020-08-21
0.589
best: 0.601 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
Text ClassificationonSemEval 2018 Task 1E-c
Macro-F1· 2020-08-21
0.563
best: 0.578 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
Text ClassificationonSemEval 2018 Task 1E-c
Micro-F1· 2020-08-21
0.707
best: 0.713 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
Emotion ClassificationonSemEval 2018 Task 1E-c
Accuracy· 2020-08-21
0.589
best: 0.601 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
Emotion ClassificationonSemEval 2018 Task 1E-c
Macro-F1· 2020-08-21
0.563
best: 0.578 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
Emotion ClassificationonSemEval 2018 Task 1E-c
Micro-F1· 2020-08-21
0.707
best: 0.713 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378

Methodology3 results

ClassificationonSemEval 2018 Task 1E-c
Accuracy· 2020-08-21
0.589
best: 0.601 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
ClassificationonSemEval 2018 Task 1E-c
Macro-F1· 2020-08-21
0.563
best: 0.578 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378
ClassificationonSemEval 2018 Task 1E-c
Micro-F1· 2020-08-21
0.707
best: 0.713 (SpanEmo)
SOTA
EmoGraph: Capturing Emotion Correlations using Graph Networks arXiv:2008.09378