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Semantic Relatedness on SICK 2014 (test)

0.884Pearson's r

InferSent

Updated 3d ago

Evaluation Results

Method
InferSent 2018.08	0.884	-	-
HBMP 2018.08	0.876	-	-
HBMP 2018.08	0.876	-	-
ReSAN 2018.01	0.872	0.8163	0.2623
DISAN 2018.01	0.8695	0.8139	0.2879
Dependency Tree-LSTM 2015.02	0.8676	0.8083	0.2532
Dep. Tree-LSTM 2018.01	0.8676	0.8083	0.2532
Dependency Tree-LSTM 2017.07	0.8676	0.8083	0.2532
Dependency Tree-LSTM 2015.06	0.8676	0.8083	0.2532
combine-skip+COCO 2015.06	0.8655	0.7995	0.2561
combine-skip 2017.07	0.8584	0.7916	0.2687
combine-skip 2015.06	0.8584	0.7916	0.2687
Constituency Tree-LSTM 2015.02	0.8582	0.7966	0.2734
Cons. Tree-LSTM 2018.01	0.8582	0.7966	0.2734
SkipThought 2018.08	0.858	-	-
Bi-GRU 2018.01	0.8572	0.8026	0.3079
Bidirectional LSTM 2015.02	0.8567	0.7966	0.2736
Bidirectional LSTM 2017.07	0.8567	0.7966	0.2736
Bidirectional LSTM 2015.06	0.8567	0.7966	0.2736
2-layer Bidirectional LSTM 2015.02	0.8558	0.7965	0.2762
LSTM 2015.02	0.8528	0.7911	0.2831
LSTM 2017.07	0.8528	0.7911	0.2831
LSTM 2015.06	0.8528	0.7911	0.2831
Multi-head 2018.01	0.8521	0.7942	0.3258
2-layer LSTM 2015.02	0.8515	0.7896	0.2838
uni-skip 2015.06	0.8477	0.778	0.2872
Bi-LSTM 2018.01	0.8473	0.7913	0.3276
Hierarchical CNN 2018.01	0.8436	0.7874	0.3162
ECNU 2015.02	0.8414	-	-
ECNU 2018.01	0.8414	-	-
ECNU 2015.06	0.8414	-	-
bi-skip 2015.06	0.8405	0.7696	0.2995
Doc2VecC 2017.07	0.8381	0.7621	0.3053
Multi-window CNN 2018.01	0.8374	0.7793	0.3395
ECNU 2017.07	0.8279	0.7689	0.325
Meaning Factory 2015.02	0.8268	0.7721	0.3224
Meaning Factory 2018.01	0.8268	0.7721	0.3224
Meaning Factory 2017.07	0.8268	0.7721	0.3224
Meaning Factory 2015.06	0.8268	0.7721	0.3224
UNAL-NLP 2015.02	0.807	0.7489	0.355
UNAL-NLP 2017.07	0.807	0.7489	0.355
UNAL-NLP 2015.06	0.807	0.7489	0.355
Illinois-LH 2015.02	0.7993	0.7538	0.3692
Illinois-LH 2017.07	0.7993	0.7538	0.3692
Illinois-LH 2015.06	0.7993	0.7538	0.3692
DT-RNN 2015.02	0.7923	0.7319	0.3822
DT-RNN 2018.01	0.7923	0.7319	0.3822
DT-RNN 2017.07	0.7923	0.7319	0.3822
DT-RNN 2015.06	0.7923	0.7319	0.3822
SDT-RNN 2015.02	0.79	0.7304	0.3848
SDT-RNN 2018.01	0.79	0.7304	0.3848
SDT-RNN 2017.07	0.79	0.7304	0.3848
SDT-RNN 2015.06	0.79	0.7304	0.3848
Mean vectors 2015.02	0.7577	0.6738	0.4557
Mean vectors 2017.07	0.7577	0.6738	0.4557
Mean vectors 2015.06	0.7577	0.6738	0.4557