  Robust dialogue belief tracking is a key component in maintaining good
quality dialogue systems. The tasks that dialogue systems are trying to solve
are becoming increasingly complex, requiring scalability to multi domain,
semantically rich dialogues. However, most current approaches have difficulty
scaling up with domains because of the dependency of the model parameters on
the dialogue ontology. In this paper, a novel approach is introduced that fully
utilizes semantic similarity between dialogue utterances and the ontology
terms, allowing the information to be shared across domains. The evaluation is
performed on a recently collected multi-domain dialogues dataset, one order of
magnitude larger than currently available corpora. Our model demonstrates great
capability in handling multi-domain dialogues, simultaneously outperforming
existing state-of-the-art models in single-domain dialogue tracking tasks.
