  Many task-oriented dialogue systems use deep reinforcement learning (DRL) to
learn policies that respond to the user appropriately and complete the tasks
successfully. Training DRL agents with diverse dialogue trajectories prepare
them well for rare user requests and unseen situations. One effective
diversification method is to let the agent interact with a diverse set of
learned user models. However, trajectories created by these artificial user
models may contain generation errors, which can quickly propagate into the
agent's policy. It is thus important to control the quality of the
diversification and resist the noise. In this paper, we propose a novel
dialogue diversification method for task-oriented dialogue systems trained in
simulators. Our method, Intermittent Short Extension Ensemble (I-SEE),
constrains the intensity to interact with an ensemble of diverse user models
and effectively controls the quality of the diversification. Evaluations on the
Multiwoz dataset show that I-SEE successfully boosts the performance of several
state-of-the-art DRL dialogue agents.
