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Zero-Shot Learning for Semantic Utterance Classification
[article]
2014
arXiv
pre-print
We propose a novel zero-shot learning method for semantic utterance classification (SUC). It learns a classifier f: X → Y for problems where none of the semantic categories Y are present in the training set. The framework uncovers the link between categories and utterances using a semantic space. We show that this semantic space can be learned by deep neural networks trained on large amounts of search engine query log data. More precisely, we propose a novel method that can learn discriminative
arXiv:1401.0509v3
fatcat:bcri3qreyfdqtm3npsmfat77m4