@inproceedings{sharoff-2017-toward,
    title = "Toward Pan-{S}lavic {NLP}: Some Experiments with Language Adaptation",
    author = "Sharoff, Serge",
    editor = "Erjavec, Toma{\v{z}}  and
      Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-1401",
    doi = "10.18653/v1/W17-1401",
    pages = "1--2",
    abstract = "There is great variation in the amount of NLP resources available for Slavonic languages. For example, the Universal Dependency treebank (Nivre et al., 2016) has about 2 MW of training resources for Czech, more than 1 MW for Russian, while only 950 words for Ukrainian and nothing for Belorussian, Bosnian or Macedonian. Similarly, the Autodesk Machine Translation dataset only covers three Slavonic languages (Czech, Polish and Russian). In this talk I will discuss a general approach, which can be called Language Adaptation, similarly to Domain Adaptation. In this approach, a model for a particular language processing task is built by lexical transfer of cognate words and by learning a new feature representation for a lesser-resourced (recipient) language starting from a better-resourced (donor) language. More specifically, I will demonstrate how language adaptation works in such training scenarios as Translation Quality Estimation, Part-of-Speech tagging and Named Entity Recognition.",
}