# Zero-Shot Learning for Semantic Utterance Classification

arXiv: https://arxiv.org/abs/1401.0509 · HTML: https://mlsanity.com/paper/1401.0509/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2013.

## Built on by (most cited first, 5 of 5)

- [Generalized Zero-shot Intent Detection via Commonsense Knowledge](https://mlsanity.com/paper/2102.02925/) (2021)
- [Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation](https://mlsanity.com/paper/2105.03544/) (2021)
- [Unsupervised Spoken Utterance Classification](https://mlsanity.com/paper/2107.01068/) (2021)
- [Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users](https://mlsanity.com/paper/2110.13213/) (2021)
- [Leveraging Contextual Relatedness to Identify Suicide Documentation in Clinical Notes through Zero Shot Learning](https://mlsanity.com/paper/2301.03531/) (2023)

## Abstract (arXiv)

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 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.