- Tytuł:
- Modeling morphological learning, typology, and change : What can the neural sequence-to-sequence framework contribute?
- Autorzy:
-
Elsner, Micha
Sims, Andrea D.
Erdmann, Alexander
Hernandez, Antonio
Jaffe, Evan
Jin, Lifeng
Booker Johnson, Martha
Karim, Shuan
King, David L.
Lamberti Nunes, Luana
Oh, Byung-Doh
Rasmussen, Nathan
Shain, Cory
Antetomaso, Stephanie
Dickinson, Kendra V.
Diewald, Noah
McKenzie, Michelle
Stevens-Guille, Symon - Powiązania:
- https://bibliotekanauki.pl/articles/103835.pdf
- Data publikacji:
- 2019
- Wydawca:
- Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
- Tematy:
-
morphology
computational modeling
typology - Opis:
- We survey research using neural sequence-to-sequence models as computational 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.
- Źródło:
-
Journal of Language Modelling; 2019, 7, 1; 53-98
2299-856X
2299-8470 - Pojawia się w:
- Journal of Language Modelling
- Dostawca treści:
- Biblioteka Nauki
Artykuł