  Dealing with previously unseen slots is a challenging problem in a real-world
multi-domain dialogue state tracking task. Other approaches rely on predefined
mappings to generate candidate slot keys, as well as their associated values.
This, however, may fail when the key, the value, or both, are not seen during
training. To address this problem we introduce a neural network that leverages
external knowledge bases (KBs) to better classify out-of-vocabulary slot keys
and values. This network projects the slot into an attribute space derived from
the KB, and, by leveraging similarities in this space, we propose candidate
slot keys and values to the dialogue state tracker. We provide extensive
experiments that demonstrate that our stratagem can improve upon a previous
approach, which relies on predefined candidate mappings. In particular, we
evaluate this approach by training a state-of-the-art model with candidates
generated from our network, and obtained relative increases of 57.7% and 82.7%
in F1 score and accuracy, respectively, for the aforementioned model, when
compared to the current candidate generation strategy.
