Zero-Shot Learning and Clustering for Semantic Utterance Classification

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 [1]. Furthermore, we achieve state-of-the-art results by combining the semantic features with a supervised method.