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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 →

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.

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: 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 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 →

Hidden Structure and Ambiguity in Phonological Learning

Published on: Author: Gaja Jarosz

Jarosz, Gaja. January 2019. Hidden Structure and Ambiguity in Phonological Learning . Second Annual Meeting of the Society for Computation in LinguisticsNew York, New York.