  Task-oriented dialogue systems can efficiently serve a large number of
customers and relieve people from tedious works. However, existing
task-oriented dialogue systems depend on handcrafted actions and states or
extra semantic labels, which sometimes degrades user experience despite the
intensive human intervention. Moreover, current user simulators have limited
expressive ability so that deep reinforcement Seq2Seq models have to rely on
selfplay and only work in some special cases. To address those problems, we
propose a uSer and Agent Model IntegrAtion (SAMIA) framework inspired by an
observation that the roles of the user and agent models are asymmetric.
Firstly, this SAMIA framework model the user model as a Seq2Seq learning
problem instead of ranking or designing rules. Then the built user model is
used as a leverage to train the agent model by deep reinforcement learning. In
the test phase, the output of the agent model is filtered by the user model to
enhance the stability and robustness. Experiments on a real-world coffee
ordering dataset verify the effectiveness of the proposed SAMIA framework.
