@InProceedings{cotterell-EtAl:2017:EMNLP2017,
  author    = {Cotterell, Ryan  and  Vylomova, Ekaterina  and  Khayrallah, Huda  and  Kirov, Christo  and  Yarowsky, David},
  title     = {Paradigm Completion for Derivational Morphology},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {714--720},
  abstract  = {The generation of complex derived word forms has been an overlooked problem in
	NLP; we fill this gap by applying neural sequence-to-sequence models to the
	task. We overview the theoretical motivation for a paradigmatic treatment of
	derivational morphology, and introduce the task of derivational paradigm
	completion as a parallel to inflectional paradigm completion. State-of-the-art
	neural models adapted from the inflection task are able to learn the range of
	derivation patterns, and outperform a non-neural baseline by 16.4%. However,
	due to semantic, historical, and lexical considerations involved in
	derivational morphology, future work will be needed to achieve performance
	parity with inflection-generating systems.},
  url       = {https://www.aclweb.org/anthology/D17-1074}
}