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. 1 introduction Theoretical morphologists have long appealed to notions of learning, or learnability, to explain language change and the varied typological patterns of the world's languages. The high-level argument is simple: all natural languages must be learned, and "unlearnable" linguistic systems cannot survive. Therefore, the learning mechanism provides constraints on what sorts of languages can exist in the world. In the realm of morphology, however, it has not proven simple to define

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