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Zero-Shot Learning for Semantic Utterance Classification

Abstract

We propose a novel zero-shot learning method for semantic utterance classification (SUC). It learns a classifier f:XYf: X \to Y for problems where none of the semantic categories YY 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 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, 2012). Furthermore, we achieve state-of-the-art results by combining the semantic features with a supervised method.

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