@inproceedings{dyer-2022-new,
    title = "New syntactic insights for automated {W}olof {U}niversal {D}ependency parsing",
    author = "Dyer, Bill",
    editor = "Moeller, Sarah  and
      Anastasopoulos, Antonios  and
      Arppe, Antti  and
      Chaudhary, Aditi  and
      Harrigan, Atticus  and
      Holden, Josh  and
      Lachler, Jordan  and
      Palmer, Alexis  and
      Rijhwani, Shruti  and
      Schwartz, Lane",
    booktitle = "Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.computel-1.2",
    doi = "10.18653/v1/2022.computel-1.2",
    pages = "5--12",
    abstract = "Focus on language-specific properties with insights from formal minimalist syntax can improve universal dependency (UD) parsing. Such improvements are especially sensitive for low-resource African languages, like Wolof, which have fewer UD treebanks in number and amount of annotations, and fewer contributing annotators. For two different UD parser pipelines, one parser model was trained on the original Wolof treebank, and one was trained on an edited treebank. For each parser pipeline, the accuracy of the edited treebank was higher than the original for both the dependency relations and dependency labels. Accuracy for universal dependency relations improved as much as 2.90{\%}, while accuracy for universal dependency labels increased as much as 3.38{\%}. An annotation scheme that better fits a language{'}s distinct syntax results in better parsing accuracy.",
}