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Research Advances In Semantic Slot Filling

This repo mainly summary latest research advances on semantic slot filling.


Thank you pay attention to the repo and it will not be updated!

Performance

Note: these results from ATIS dataset.

Model F1 Score Intent Accuracy Year
Recursive NN 0.9396 0.954 Guo et al. 2014
Joint model with recurrent intent and slot label context 0.9447 0.984 Liu and Lane, 2016b
Joint model with recurrent slot label context 0.9464 0.984 Liu and Lane, 2016b
RNN with Label Sampling 0.9489 NA Liu and Lane, 2015
Hybrid RNN 0.9506 NA Mesnil et al., 2015
RNN-EM 0.9525 NA Peng and Yao, 2015
CNN-CRF 0.9435 NA Xu and Sarikaya, 2013
Encoder-labeler Deep LSTM 0.9566 NA Kurata et al., 2016
Joint GRU model(W) 0.9549 0.9810 Zhang and Wang, 2016
Attention Encoder-Decoder NN 0.9587 0.9843 Liu and Lane, 2016a
Bi-model without a decoder 0.9665 0.9876 Wang and Shen, 2018
Bi-model with a decoder 0.9689 0.9899 Wang and Shen, 2018

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