Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?

Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?

We survey research using neural sequence-to-sequence models as compu- tational models of morphological learning and learnability. We discuss their use in determining the predictability of inflectional exponents, in making predictions about language acquisition and in modeling language change. Finall...

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Bibliographic Details
Main Authors: Micha Elsner, Andrea D. Sims, Alexander Erdmann, Antonio Hernandez, Evan Jaffe, Lifeng Jin, Martha Booker Johnson, Shuan Karim, David L. King, Luana Lamberti Nunes, Byung-Doh Oh, Nathan Rasmussen, Cory Shain, Stephanie Antetomaso, Kendra V. Dickinson, Noah Diewald, Michelle McKenzie, Symon Stevens-Guille
Format: Article
Language:English
Published: Polish Academy of Sciences 2019-12-01
Series:Journal of Language Modelling
Subjects:
morphology
computational modeling
typology
Online Access:https://jlm.ipipan.waw.pl/index.php/JLM/article/view/244
Description
Summary:We survey research using neural sequence-to-sequence models as compu- tational models of morphological learning and learnability. We discuss their use in determining the predictability of inflectional exponents, in making predictions about language acquisition and in modeling language change. Finally, we make some proposals for future work in these areas.
ISSN:2299-856X
2299-8470

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