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.
@article{arxiv.1702.06675,
title = {Context-Aware Prediction of Derivational Word-forms},
author = {Ekaterina Vylomova and Ryan Cotterell and Timothy Baldwin and Trevor Cohn},
journal= {arXiv preprint arXiv:1702.06675},
year = {2017}
}