@inproceedings{kodner-2022-modeling,
title = "Modeling the Relationship between Input Distributions and Learning Trajectories with the Tolerance Principle",
author = "Kodner, Jordan",
editor = "Chersoni, Emmanuele and
Hollenstein, Nora and
Jacobs, Cassandra and
Oseki, Yohei and
Pr{\'e}vot, Laurent and
Santus, Enrico",
booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.cmcl-1.7",
doi = "10.18653/v1/2022.cmcl-1.7",
pages = "61--67",
abstract = "Child language learners develop with remarkable uniformity, both in their learning trajectories and ultimate outcomes, despite major differences in their learning environments. In this paper, we explore the role that the frequencies and distributions of irregular lexical items in the input plays in driving learning trajectories. We conclude that while the Tolerance Principle, a type-based model of productivity learning, accounts for inter-learner uniformity, it also interacts with input distributions to drive cross-linguistic variation in learning trajectories.",
}
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%0 Conference Proceedings
%T Modeling the Relationship between Input Distributions and Learning Trajectories with the Tolerance Principle
%A Kodner, Jordan
%Y Chersoni, Emmanuele
%Y Hollenstein, Nora
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kodner-2022-modeling
%X Child language learners develop with remarkable uniformity, both in their learning trajectories and ultimate outcomes, despite major differences in their learning environments. In this paper, we explore the role that the frequencies and distributions of irregular lexical items in the input plays in driving learning trajectories. We conclude that while the Tolerance Principle, a type-based model of productivity learning, accounts for inter-learner uniformity, it also interacts with input distributions to drive cross-linguistic variation in learning trajectories.
%R 10.18653/v1/2022.cmcl-1.7
%U https://aclanthology.org/2022.cmcl-1.7
%U https://doi.org/10.18653/v1/2022.cmcl-1.7
%P 61-67
Markdown (Informal)
[Modeling the Relationship between Input Distributions and Learning Trajectories with the Tolerance Principle](https://aclanthology.org/2022.cmcl-1.7) (Kodner, CMCL 2022)
ACL