Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning 论文

摘要

End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors.