@inproceedings{cotterell-etal-2017-neural,
    title = "Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion",
    author = "Cotterell, Ryan  and
      Sylak-Glassman, John  and
      Kirov, Christo",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/E17-2120/",
    pages = "759--765",
    abstract = "Many of the world{'}s languages contain an abundance of inflected forms for each lexeme. A critical task in processing such languages is predicting these inflected forms. We develop a novel statistical model for the problem, drawing on graphical modeling techniques and recent advances in deep learning. We derive a Metropolis-Hastings algorithm to jointly decode the model. Our Bayesian network draws inspiration from principal parts morphological analysis. We demonstrate improvements on 5 languages."
}