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ResearchFormerly WizWand

Slot Filling on ATIS (test)

96.89F1 ScoreBi-model with a decoder
61.571670.740879.9189.0792Sep 6, 2016May 9, 2017Jan 9, 2018Sep 12, 2018May 15, 2019Jan 15, 2020Sep 17, 2020
Updated 8mo ago

Evaluation results

MethodLinks
Bi-model with a decoderdecoder=with2018.1296.89
Bi-model without a decoderdecoder=without2018.1296.65
Attention BIRNN2018.1295.98
Attention Encoder-Decoder NN2018.1295.87
Ourstask_setting=multi-task2019.1195.8
Attention Encoder-Decoder NN (with aligned inputs)Training mode=Independent, Aligned inputs=true2016.0995.78
Attention BiRNNTraining mode=Independent2016.0995.75
Encoder-labeler Deep LSTMTraining mode=Independent2016.0995.66
Encoder-labeler Deep LSTM2018.1295.66
AIN-1OEncoder=WORD-CHAR-BILSTM2020.0995.59
AIN-F2OEncoder=WORD-CHAR-BILSTM2020.0995.58
CRFEncoder=WORD-CHAR-BILSTM2020.0995.52
SF-IDtask_setting=single-task2019.1195.5
Joint GRU Model (W)2018.1295.49
MaxEntEncoder=WORD-CHAR-BILSTM2020.0995.47
Slotted-Gatedtask_setting=single-task2019.1195.4
CNN CRF2018.1295.35
RNN-EMTraining mode=Independent2016.0995.25
RNN-EM2018.1295.25
CAPSULE-NLUtask_setting=single-task2019.1195.2
SPTIDtask_setting=single-task2019.1195.1
GCDTtask_setting=single-task2019.1195.1
Deep LSTMTraining mode=Independent2016.0995.08
Hybrid RNNTraining mode=Independent2016.0995.06
Hybrid RNN2018.1295.06
Independent training RNN slot filling model2016.0994.91
RNN with Label SamplingTraining mode=Independent2016.0994.89
RNN with Label Sampling2018.1294.89
CVTtask_setting=single-task2019.1194.8
Joint model with recurrent slot label contextslot label context=recurrent2016.0994.64
Joint model with recurrent slot label context2018.1294.64
Joint model with local & recurrent slot label contextslot label context=local & recurrent2016.0994.52
Joint model with recurrent intent + slot label contextintent context=recurrent, slot label context=yes2016.0994.47
Joint model with recurrent intent and slot label context2018.1294.47
Joint model with local & recurrent intent + slot label contextintent context=local & recurrent, slot label context=yes2016.0994.45
CNN-CRFTraining mode=Independent2016.0994.35
Joint Seqtask_setting=single-task2019.1194.3
Joint model with local intent contextintent context=local2016.0994.22
Attention-Basedtask_setting=single-task2019.1194.2
Joint model with local & recurrent intent contextintent context=local & recurrent2016.0994.18
Joint model with recurrent intent contextintent context=recurrent2016.0994.16
Basic joint training model2016.0994.15
Joint model with local slot label contextslot label context=local2016.0994.14
Joint model with local intent + slot label contextintent context=local, slot label context=yes2016.0994.13
RecNN+Viterbi2016.0993.96
Recursive NN2018.1293.96
RecNN2016.0993.22
AIN-F2OEncoder=WORD-CNN2020.0992.26
CRFEncoder=WORD-CNN2020.0992.24
AIN-1OEncoder=WORD-CNN2020.0992.18
MaxEntEncoder=WORD-CNN2020.0991.07
AIN-F2OEncoder=WORD ONLY2020.0988.86
AIN-1OEncoder=WORD ONLY2020.0988.76
CRFEncoder=WORD ONLY2020.0976.82
MaxEntEncoder=WORD ONLY2020.0962.93