  Learning an efficient manager of dialogue agent from data with little manual
intervention is important, especially for goal-oriented dialogues. However,
existing methods either take too many manual efforts (e.g. reinforcement
learning methods) or cannot guarantee the dialogue efficiency (e.g.
sequence-to-sequence methods). In this paper, we address this problem by
proposing a novel end-to-end learning model to train a dialogue agent that can
look ahead for several future turns and generate an optimal response to make
the dialogue efficient. Our method is data-driven and does not require too much
manual work for intervention during system design. We evaluate our method on
two datasets of different scenarios and the experimental results demonstrate
the efficiency of our model.
