  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.
This method exploits the learned linguistic knowledge in transformers, without
any reference to their inner representations; hence, the technique is readily
adaptable to the continuous appearance of more powerful language models. We
show a proof-of-concept example of our proposed technique, using it to guide
unsupervised symbolic link-grammar induction methods drawn from our prior
research.
