@inproceedings{cotterell-heigold-2017-cross,
    title = "Cross-lingual Character-Level Neural Morphological Tagging",
    author = "Cotterell, Ryan  and
      Heigold, Georg",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1078/",
    doi = "10.18653/v1/D17-1078",
    pages = "748--759",
    abstract = "Even for common NLP tasks, sufficient supervision is not available in many languages {--} morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones."
}