Fully Statistical Neural Belief Tracking
Nikola Mrkšić, Ivan Vulić · 2018
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.