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# Neural Belief Tracker
Contact: Nikola Mrkšić ([email protected])
An implementation of the Fully Data-Driven version of the Neural Belief Tracking (NBT) model (ACL 2018, [Fully Statistical Neural Belief Tracking](https://arxiv.org/abs/1805.11350)).
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.
### Configuring the Tool
The config file in the config directory specifies the model hyperparameters, training details, dataset, ontologies, etc.
### Running Experiments
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.
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