A Model of Zero-Shot Learning of Spoken Language Understanding

When building spoken dialogue systems for a new domain, a major bottleneck is developing a spoken language understand-ing (SLU) module that handles the new domain’s terminology and semantic con-cepts. We propose a statistical SLU model that generalises to both previously unseen input words and previously unseen out-put classes by leveraging unlabelled data. After mapping the utterance into a vector space, the model exploits the structure of the output labels by mapping each label to a hyperplane that separates utterances with and without that label. Both these mappings are initialised with unsupervised word embeddings, so they can be com-puted even for words or concepts which were not in the SLU training data.