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

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

Journal of Language Modelling

2019

Vol. 7, No. 1

53--98

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.