  The recent advent of neural approaches for developing each dialog component
in task-oriented dialog systems has remarkably improved, yet optimizing the
overall system performance remains a challenge. Besides, previous research on
modeling complicated multi-domain goal-oriented dialogs in end-to-end fashion
has been limited. In this paper, we present an effective multi-domain
end-to-end trainable neural dialog system SUMBT+LaRL that incorporates two
previous strong models and facilitates them to be fully differentiable.
Specifically, the SUMBT+ estimates user-acts as well as dialog belief states,
and the LaRL models latent system action spaces and generates responses given
the estimated contexts. We emphasize that the training framework of three steps
significantly and stably increase dialog success rates: separately pretraining
the SUMBT+ and LaRL, fine-tuning the entire system, and then reinforcement
learning of dialog policy. We also introduce new reward criteria of
reinforcement learning for dialog policy training. Then, we discuss
experimental results depending on the reward criteria and different dialog
evaluation methods. Consequently, our model achieved the new state-of-the-art
success rate of 85.4% on corpus-based evaluation, and a comparable success rate
of 81.40% on simulator-based evaluation provided by the DSTC8 challenge. To our
best knowledge, our work is the first comprehensive study of a modularized E2E
multi-domain dialog system that learning from each component to the entire
dialog policy for task success.
