  Rule-based dialogue management is still the most popular solution for
industrial task-oriented dialogue systems for their interpretablility. However,
it is hard for developers to maintain the dialogue logic when the scenarios get
more and more complex. On the other hand, data-driven dialogue systems, usually
with end-to-end structures, are popular in academic research and easier to deal
with complex conversations, but such methods require plenty of training data
and the behaviors are less interpretable. In this paper, we propose a method to
leverages the strength of both rule-based and data-driven dialogue managers
(DM). We firstly introduce the DM of Carina Dialog System (CDS, an advanced
industrial dialogue system built by Microsoft). Then we propose the
"model-trigger" design to make the DM trainable thus scalable to scenario
changes. Furthermore, we integrate pre-trained models and empower the DM with
few-shot capability. The experimental results demonstrate the effectiveness and
strong few-shot capability of our method.
