Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?
Keywords:morphology, computational modeling, typology
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.
How to Cite
Copyright (c) 2019 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
This work is licensed under a Creative Commons Attribution 3.0 Unported License.