End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning
arXiv: Computation and Language, Volume abs/1606.01269, 2016.
This paper presents a model for end-to-end learning of task-oriented dialog systems. The main component of the model is a recurrent neural network (an LSTM), which maps from raw dialog history directly to a distribution over system actions. The LSTM automatically infers a representation of dialog history, which relieves the system develop...More
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