Zero-shot learning for semantic utterance classification

Yann N. Dauphin, Gokhan Tur, Dilek Hakkani-Tür, Larry Heck

Research output: Contribution to conferencePaperpeer-review


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 through 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. What’s more, we propose a novel method that can learn discriminative semantic features without supervision. It uses the zero-shot learning framework to guide the learning of the semantic features. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by (Tur et al., 2012). Furthermore, we achieve state-of-the-art results by combining the semantic features with a supervised method.

Original languageEnglish (US)
StatePublished - 2014
Externally publishedYes
Event2nd International Conference on Learning Representations, ICLR 2014 - Banff, Canada
Duration: Apr 14 2014Apr 16 2014


Conference2nd International Conference on Learning Representations, ICLR 2014

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Education
  • Computer Science Applications


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