Author: Gaja Jarosz

Incremental Learning of Lexically-Specific Morphophonology: an Integrative Approach

Published on Author Gaja Jarosz

Jarosz, Gaja. to appear. Incremental Learning of Lexically-Specific Morphophonology: an Integrative Approach. To appear in Linguistics Vanguard. Acquisition and processing results indicate idiosyncratic, lexical knowledge interacts with productive, grammatical knowledge in systematic ways. Evidence from language processing demonstrates that higher frequency and less productive complex words are more likely to be retrieved holistically from the… Continue reading Incremental Learning of Lexically-Specific Morphophonology: an Integrative Approach →

Pattern Structure Modulates Learning of Lexically Conditioned Morphology

Published on Author Gaja Jarosz

Hughes, Cerys and Jarosz, Gaja. Pattern Structure Modulates Learning of Lexically Conditioned Morphology. 2024. Poster presented at Architectures and Mechanisms for Language Processing (AMLaP 2024) in Edinburgh, Scotland, September 5-7, 2024. Poster. Work in first language acquisition and artificial grammar learning (AGL) indicates thatlanguage learners can extract systematic regularities from inconsistent language data. Insome cases,… Continue reading Pattern Structure Modulates Learning of Lexically Conditioned Morphology →

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 Type and Token Frequency Jointly Drive Learning of Morphology →

Learning syntactic parameters without triggers by assigning credit and blame

Published on Author Gaja Jarosz

Brandon Prickett, Kaden Holladay, Shay Hucklebridge, Max Nelson, Rajesh Bhatt, Gaja Jarosz, Kyle Johnson, Aleksei Nazarov, and Joe Pater. 2019. Learning syntactic parameters without triggers by assigning credit and blame. Proceedings of the 55th Annual Meeting of the Chicago Linguistic Society (CLS), Chicago, Illinois.

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 The Credit Problem in Parametric Stress: A Statistical Approach →

Generalizing from Inconsistent Data: The Combined Roles of Type and Token Frequency

Published on Author Gaja Jarosz

Gaja Jarosz. 2023. Generalizing from Inconsistent Data: The Combined Roles of Type and Token Frequency. Colloquium talk, Yale University, New Haven, CT. November 2023. Language acquisition proceeds on the basis of incomplete, ambiguous linguistic input. Due to recent developments in computational modeling of morphophonological learning, there now exist numerous approaches for learning of various kinds… Continue reading Generalizing from Inconsistent Data: The Combined Roles of Type and Token Frequency →

Generalizing from Inconsistent Data: How much do Exceptions Count? (AMP 2022 Plenary)

Published on Author Gaja Jarosz

Language acquisition proceeds on the basis of incomplete, ambiguous linguistic input, and one source of this ambiguity is hidden phonological structure. Due to recent developments in computational modeling of phonological learning, there now exist numerous approaches for learning of various kinds of hidden phonological structure from incomplete, unlabeled, and noisy data. These computational models make it… Continue reading Generalizing from Inconsistent Data: How much do Exceptions Count? (AMP 2022 Plenary) →

Generalizing Phonological (Hidden) Structure (USC Talk & Minicourse)

Published on Author Gaja Jarosz

Language acquisition proceeds on the basis of incomplete, ambiguous linguistic input, and one source of this ambiguity is hidden phonological structure. Due to recent developments in computational modeling of phonological learning, there now exist numerous approaches for learning of various kinds of hidden phonological structure from incomplete, unlabeled, and noisy data. These computational models make it… Continue reading Generalizing Phonological (Hidden) Structure (USC Talk & Minicourse) →