Tag Archives: Expectation Driven Learning

Type and Token Frequency Jointly Drive Learning of Morphology

Published on: Author: Gaja Jarosz

Jarosz, Gaja, Cerys Hughes, Andrew Lamont, Brandon Prickett, Maggie Baird, Seoyoung Kim & Max Nelson. 2024. Type and Token Frequency Jointly Drive Learning of Morphology. OSF. https://doi.org/10.31234/osf.io/mbp24. We examine the joint roles of type frequency and token frequency in three artificial language learning experiments involving lexicalized plural allomorphy. The primary role of type frequency in… Continue reading →

The Credit Problem in Parametric Stress: A Statistical Approach

Published on: Author: Gaja Jarosz

Aleksei Nazarov & Gaja Jarosz. 2021. The Credit Problem in Parametric Stress: A Statistical Approach. Glossa 6(1). 1-26. https://doi.org/10.16995/glossa.5884 In this paper, we introduce a novel domain-general, statistical learning model for P&P grammars: the Expectation Driven Parameter Learner (EDPL). We show that the EDPL provides a mathematically principled solution to the Credit Problem (Dresher 1999).… Continue reading →