Journal of Language Modelling (Dec 2019)
Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?
- 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
Affiliations
- Micha Elsner
- ORCiD
- The Ohio State University
- Andrea D. Sims
- ORCiD
- The Ohio State University
- Alexander Erdmann
- The Ohio State University
- Antonio Hernandez
- The Ohio State University
- Evan Jaffe
- The Ohio State University
- Lifeng Jin
- The Ohio State University
- Martha Booker Johnson
- ORCiD
- The Ohio State University
- Shuan Karim
- The Ohio State University
- David L. King
- ORCiD
- The Ohio State University
- Luana Lamberti Nunes
- ORCiD
- The Ohio State University
- Byung-Doh Oh
- The Ohio State University
- Nathan Rasmussen
- The Ohio State University
- Cory Shain
- The Ohio State University
- Stephanie Antetomaso
- The Ohio State University
- Kendra V. Dickinson
- The Ohio State University
- Noah Diewald
- ORCiD
- The Ohio State University
- Michelle McKenzie
- The Ohio State University
- Symon Stevens-Guille
- The Ohio State University
- DOI
- https://doi.org/10.15398/jlm.v7i1.244
- Journal volume & issue
-
Vol. 7,
no. 1
pp. 53–98 – 53–98
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.
Keywords
