@inproceedings{sandhan-etal-2023-systematic,
    title = "Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing",
    author = "Sandhan, Jivnesh  and
      Behera, Laxmidhar  and
      Goyal, Pawan",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.eacl-main.158/",
    doi = "10.18653/v1/2023.eacl-main.158",
    pages = "2164--2171",
    abstract = "In this work, we focus on low-resource dependency parsing for multiple languages. Several strategies are tailored to enhance performance in low-resource scenarios. While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language that we are interested in, and not much attention has been given to measuring the efficacy of these strategies. We experiment with 5 low-resource strategies for our ensembled approach on 7 Universal Dependency (UD) low-resource languages. Our exhaustive experimentation on these languages supports the effective improvements for languages not covered in pretrained models. We show a successful application of the ensembled system on a truly low-resource language Sanskrit. The code and data are available at: \url{https://github.com/Jivnesh/SanDP}"
}