Modelling a subregular bias in phonological learning with Recurrent Neural Networks

Authors

  • Brandon Prickett University of Massachusetts Amherst

Keywords:

neural networks, learning bias, Formal Language Theory, phonology

Abstract

A number of experiments have demonstrated what seems to be a bias in human phonological learning for patterns that are simpler according to Formal Language Theory (Finley and Badecker 2008; Lai 2015; Avcu 2018). This paper demonstrates that a sequence-to-sequence neural network (Sutskever et al. 2014), which has no such restriction explicitly built into its architecture, can successfully capture this bias. These results suggest that a bias for patterns that are simpler according to Formal Language Theory may not need to be explicitly incorporated into models of phonological learning.

Full article

Published

2021-08-10

How to Cite

Prickett, B. (2021). Modelling a subregular bias in phonological learning with Recurrent Neural Networks. Journal of Language Modelling, 9(1). Retrieved from https://jlm.ipipan.waw.pl/index.php/JLM/article/view/251