@inproceedings{samardzic-etal-2022-language,
    title = "On Language Spaces, Scales and Cross-Lingual Transfer of {UD} Parsers",
    author = {Samard{\v{z}}i{\'c}, Tanja  and
      Gutierrez-Vasques, Ximena  and
      van der Goot, Rob  and
      M{\"u}ller-Eberstein, Max  and
      Pelloni, Olga  and
      Plank, Barbara},
    booktitle = "Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.conll-1.18",
    pages = "266--281",
    abstract = "Cross-lingual transfer of parsing models has been shown to work well for several closely-related languages, but predicting the success in other cases remains hard. Our study is a comprehensive analysis of the impact of linguistic distance on the transfer of UD parsers. As an alternative to syntactic typological distances extracted from URIEL, we propose three text-based feature spaces and show that they can be more precise predictors, especially on a more local scale, when only shorter distances are taken into account. Our analyses also reveal that the good coverage in typological databases is not among the factors that explain good transfer.",
}