  This paper presents a hybrid dialog state tracker enhanced by trainable
Spoken Language Understanding (SLU) for slot-filling dialog systems. Our
architecture is inspired by previously proposed neural-network-based
belief-tracking systems. In addition, we extended some parts of our modular
architecture with differentiable rules to allow end-to-end training. We
hypothesize that these rules allow our tracker to generalize better than pure
machine-learning based systems. For evaluation, we used the Dialog State
Tracking Challenge (DSTC) 2 dataset - a popular belief tracking testbed with
dialogs from restaurant information system. To our knowledge, our hybrid
tracker sets a new state-of-the-art result in three out of four categories
within the DSTC2.
