Graded Grammaticality in Prediction Fractal Machines

Parfitt, Shan, Tiño, Peter, Dorffner, Georg

Neural Information Processing Systems 

We introduce a novel method of constructing language models, which avoids some of the problems associated with recurrent neural networks.The method of creating a Prediction Fractal Machine (PFM) [1] is briefly described and some experiments are presented which demonstrate the suitability of PFMs for language modeling. PFMs distinguish reliably between minimal pairs, and their behavior isconsistent with the hypothesis [4] that wellformedness is'graded' not absolute. A discussion of their potential to offer fresh insights into language acquisition and processing follows. 1 Introduction Cognitive linguistics has seen the development in recent years of two important, related trends.

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