Multitask Learning for Spoken Language Understanding

Abstract

In this paper, we present a multitask learning (MTL) method for intent classification in goal oriented human-machine spoken dialog systems. MTL aims at training tasks in parallel while using a shared representation. What is learned for each task can help other tasks be learned better. Our goal is to automatically re-use the existing labeled data from various applications, which are similar but may have different intents or intent distributions, in order to improve the performance. For this purpose, we propose an automated intent mapping algorithm across applications. We also propose employing active learning to selectively sample the data to be re-used. Our results indicate that we can achieve significant improvements in intent classification performance especially when the labeled data size is limited.