no code implementations • 26 May 2020 • Ben Goertzel, Andres Suarez Madrigal, Gino Yu
A novel approach to automated learning of syntactic rules governing natural languages is proposed, based on using probabilities assigned to sentences (and potentially longer word sequences) by transformer neural network language models to guide symbolic learning processes like clustering and rule induction.