Corpus ID: 12923236

End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning

@article{Williams2016EndtoendLD,
  title={End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning},
  author={J. Williams and G. Zweig},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.01269}
}
This paper presents a model for end-to-end learning of task-oriented dialog systems. [...] Key Method The LSTM automatically infers a representation of dialog history, which relieves the system developer of much of the manual feature engineering of dialog state. In addition, the developer can provide software that expresses business rules and provides access to programmatic APIs, enabling the LSTM to take actions in the real world on behalf of the user.Expand
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