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

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

• 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.

• 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.

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

• 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.

• 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

• 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.

• 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