@InProceedings{sharoff:2017:BSNLP,
  author    = {Sharoff, Serge},
  title     = {Toward Pan-Slavic NLP: Some Experiments with Language Adaptation},
  booktitle = {Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-1401}
}