End-to-end joint learning of natural language understanding and dialogue manager

@article{Yang2017EndtoendJL,
  title={End-to-end joint learning of natural language understanding and dialogue manager},
  author={Xuesong Yang and Yun-Nung Chen and Dilek Z. Hakkani-T{\"u}r and Paul Crook and Xiujun Li and Jianfeng Gao and Li Deng},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={5690-5694}
}
Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by… CONTINUE READING
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