@inproceedings{kurniawan-etal-2021-ppt,
    title = "{PPT}: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation",
    author = "Kurniawan, Kemal  and
      Frermann, Lea  and
      Schulz, Philip  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-main.254",
    doi = "10.18653/v1/2021.eacl-main.254",
    pages = "2907--2918",
    abstract = "Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple {`}direct transfer{'} of a learned model based on a multilingual input encoding has provided a strong benchmark. This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language. The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer. With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach. We provide analyses of the choice of source languages for multi-source transfer, and the advantage of non-projective parsing. Our code is available online.",
}