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- W2251416368 abstract "When building spoken dialogue systems for a new domain, a major bottleneck is developing a spoken language understanding (SLU) module that handles the new domain’s terminology and semantic concepts. We propose a statistical SLU model that generalises to both previously unseen input words and previously unseen output 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 computed even for words or concepts which were not in the SLU training data." @default.
- W2251416368 created "2016-06-24" @default.
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- W2251416368 date "2015-01-01" @default.
- W2251416368 modified "2023-09-27" @default.
- W2251416368 title "A Model of Zero-Shot Learning of Spoken Language Understanding" @default.
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- W2251416368 doi "https://doi.org/10.18653/v1/d15-1027" @default.
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