  Recent reinforcement learning algorithms for task-oriented dialogue system
absorbs a lot of interest. However, an unavoidable obstacle for training such
algorithms is that annotated dialogue corpora are often unavailable. One of the
popular approaches addressing this is to train a dialogue agent with a user
simulator. Traditional user simulators are built upon a set of dialogue rules
and therefore lack response diversity. This severely limits the simulated cases
for agent training. Later data-driven user models work better in diversity but
suffer from data scarcity problem. To remedy this, we design a new corpus-free
framework that taking advantage of their benefits. The framework builds a user
simulator by first generating diverse dialogue data from templates and then
build a new State2Seq user simulator on the data. To enhance the performance,
we propose the State2Seq user simulator model to efficiently leverage dialogue
state and history. Experiment results on an open dataset show that our user
simulator helps agents achieve an improvement of 6.36% on success rate.
State2Seq model outperforms the seq2seq baseline for 1.9 F-score.
