@InProceedings{moeller-EtAl:2018:W18-48,
  author    = {Moeller, Sarah  and  Kazeminejad, Ghazaleh  and  Cowell, Andrew  and  Hulden, Mans},
  title     = {A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer},
  booktitle = {Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {12--20},
  abstract  = {We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).},
  url       = {http://www.aclweb.org/anthology/W18-4802}
}