@inproceedings{barry-etal-2019-cross,
    title = "Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A {F}aroese Case Study",
    author = "Barry, James  and
      Wagner, Joachim  and
      Foster, Jennifer",
    editor = "Cherry, Colin  and
      Durrett, Greg  and
      Foster, George  and
      Haffari, Reza  and
      Khadivi, Shahram  and
      Peng, Nanyun  and
      Ren, Xiang  and
      Swayamdipta, Swabha",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D19-6118/",
    doi = "10.18653/v1/D19-6118",
    pages = "163--174",
    abstract = "Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. It is a crucial component for inducing dependency parsers in low-resource scenarios where no training data for a language exists. Using Faroese as the target language, we compare two approaches using annotation projection: first, projecting from multiple monolingual source models; second, projecting from a single polyglot model which is trained on the combination of all source languages. Furthermore, we reproduce multi-source projection (Tyers et al., 2018), in which dependency trees of multiple sources are combined. Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side. We find that polyglot training on the source languages produces an overall trend of better results on the target language but the single best result for the target language is obtained by projecting from monolingual source parsing models and then training multi-treebank POS tagging and parsing models on the target side."
}