Continuous Time Recurrent Neural Networks for Grammatical Induction

Abstract

In this paper we explore continuous time recurrent networks for grammatical induction. A higher-level generating/processing scheme can be used to tackle the grammar induction problem. Experiments are performed on several types of grammars, including a family of languages known as Tomita languages and a context-free language. The system and the experiments demonstrate that continuous time recurrent networks can learn certain grammatical induction tasks.