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

Internet

https://jlm.ipipan.waw.pl/index.php/JLM/article/view/244

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