  In this work, we present a framework for incorporating descriptive logical
rules in state-of-the-art neural networks, enabling them to learn how to handle
unseen labels without the introduction of any new training data. The rules are
integrated into existing networks without modifying their architecture, through
an additional term in the network's loss function that penalizes states of the
network that do not obey the designed rules. As a case of study, the framework
is applied to an existing neural-based Dialog State Tracker. Our experiments
demonstrate that the inclusion of logical rules allows the prediction of unseen
labels, without deteriorating the predictive capacity of the original system.
