Contact: Nikola Mrkšić (nikola.mrksic@gmail.com)
An implementation of the Fully Data-Driven version of the Neural Belief Tracking (NBT) model (ACL 2018, Fully Statistical Neural Belief Tracking).
This version of the model uses a learned belief state update in place of the rule-based mechanism used in the original paper. Requests are not a focus of this paper and should be ignored in the output.
The config file in the config directory specifies the model hyperparameters, training details, dataset, ontologies, etc.
train.sh and test.sh can be used to train and test the model (using the default config file). track.sh uses the trained models to 'simulate' a conversation where the developer can enter sequential user turns and observe the change in belief state.