  Recently, reinforcement learning (RL) has been applied to task-oriented
dialogue systems by using latent actions to solve shortcomings of supervised
learning (SL). In this paper, we propose a multi-domain task-oriented dialogue
system, called Dialogue System with Optimizing a Recurrent Action Policy using
Efficient Context (DORA), that uses SL, with subsequently applied RL to
optimize dialogue systems using a recurrent dialogue policy. This dialogue
policy recurrently generates explicit system actions as a both word-level and
high-level policy. As a result, DORA is clearly optimized during both SL and RL
steps by using an explicit system action policy that considers an efficient
context instead of the entire dialogue history. The system actions are both
interpretable and controllable, whereas the latent actions are not. DORA
improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on
MultiWOZ 2.1.

Computation and Language