  This paper proposes an improvement to the existing data-driven Neural Belief
Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT
model uses a hand-crafted belief state update mechanism which involves an
expensive manual retuning step whenever the model is deployed to a new dialogue
domain. We show that this update mechanism can be learned jointly with the
semantic decoding and context modelling parts of the NBT model, eliminating the
last rule-based module from this DST framework. We propose two different
statistical update mechanisms and show that dialogue dynamics can be modelled
with a very small number of additional model parameters. In our DST evaluation
over three languages, we show that this model achieves competitive performance
and provides a robust framework for building resource-light DST models.
