  Derivational morphology is a fundamental and complex characteristic of
language. In this paper we propose the new task of predicting the derivational
form of a given base-form lemma that is appropriate for a given context. We
present an encoder--decoder style neural network to produce a derived form
character-by-character, based on its corresponding character-level
representation of the base form and the context. We demonstrate that our model
is able to generate valid context-sensitive derivations from known base forms,
but is less accurate under a lexicon agnostic setting.
