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

Authors

Keywords:

morphology, computational modeling, typology

Abstract

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.

DOI:

https://doi.org/10.15398/jlm.v7i1.244

Full article

Published

2019-12-19

How to Cite

Elsner, M., Sims, A. D., Erdmann, A., Hernandez, A., Jaffe, E., Jin, L., Johnson, M. B., Karim, S., King, D. L., Lamberti Nunes, L., Oh, B.-D., Rasmussen, N., Shain, C., Antetomaso, S., Dickinson, K. V., Diewald, N., McKenzie, M., & Stevens-Guille, S. (2019). Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?. Journal of Language Modelling, 7(1), 53–98. https://doi.org/10.15398/jlm.v7i1.244

Issue

Vol. 7 No. 1 (2019)

Section

Overviews

License

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

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.