Research Advances in Semantic Slot Filling

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

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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

Related Papers

2010

• Tur, Gokhan, Dilek Hakkani-Tür, and Larry Heck. "What is left to be understood in ATIS?." Spoken Language Technology Workshop (SLT), 2010 IEEE. IEEE, 2010.

2013

• Mesnil, Grégoire, et al. "Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding." Interspeech. 2013.

• Yao, Kaisheng, et al. "Recurrent neural networks for language understanding." Interspeech. 2013.

2014

• Yao, Kaisheng, et al. "Spoken language understanding using long short-term memory neural networks." Spoken Language Technology Workshop (SLT), 2014 IEEE. IEEE, 2014.

2015

• Peng, Baolin, and Kaisheng Yao. "Recurrent neural networks with external memory for language understanding." arXiv preprint arXiv:1506.00195 (2015).

• Mesnil, Grégoire, et al. "Using recurrent neural networks for slot filling in spoken language understanding." IEEE/ACM Transactions on Audio, Speech, and Language Processing 2015

• Liu, B., and Lane, I. 2015. Recurrent neural network structured output prediction for spoken language understanding. In NIPS Workshop.

• Peng, B., and Yao, K. 2015. Recurrent neural networks with external memory for language understanding.arXiv.

2016

• Vu, Ngoc Thang. "Sequential convolutional neural networks for slot filling in spoken language understanding." arXiv preprint arXiv:1606.07783 (2016).

• Vu, Ngoc Thang, et al. "Bi-directional recurrent neural network with ranking loss for spoken language understanding." Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. Ieee, 2016.

• Hakkani-Tür, Dilek, et al. "Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM." Interspeech 2016

• Zhang, Xiaodong, and Houfeng Wang. "A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding." IJCAI 2016

• Liu, Bing, and Ian Lane. "Attention-based recurrent neural network models for joint intent detection and slot filling." Interspeech 2016

• Kurata, Gakuto, et al. "Leveraging sentence-level information with encoder lstm for semantic slot filling." EMNLP 2016

2017

• Zhang, Yuhao, et al. "Position-aware attention and supervised data improve slot filling." Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017.

• Zhao, Tiancheng, et al. "Generative encoder-decoder models for task-oriented spoken dialog systems with chatting capability." arXiv preprint arXiv:1706.08476 (2017).

• Zhai, Feifei, et al. "Neural Models for Sequence Chunking." AAAI. 2017.

• Zhu, Su, and Kai Yu. "Encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding." 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017.

2018

• Constantin, Stefan, Jan Niehues, and Alex Waibel. "Multi-task learning to improve natural language understanding." 2018

• Zhao, Lin, and Zhe Feng. "Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention." ACL 2018

• Wang, Yu, Yilin Shen, and Hongxia Jin. "A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling." Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Vol. 2. 2018.

• Gong, Yu, et al. "Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant." 2019