@inproceedings{kurniawan-etal-2021-ptst,
    title = "{PTST}-{U}o{M} at {S}em{E}val-2021 Task 10: Parsimonious Transfer for Sequence Tagging",
    author = "Kurniawan, Kemal  and
      Frermann, Lea  and
      Schulz, Philip  and
      Cohn, Trevor",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.semeval-1.54/",
    doi = "10.18653/v1/2021.semeval-1.54",
    pages = "445--451",
    abstract = "This paper describes PTST, a source-free unsupervised domain adaptation technique for sequence tagging, and its application to the SemEval-2021 Task 10 on time expression recognition. PTST is an extension of the cross-lingual parsimonious parser transfer framework, which uses high-probability predictions of the source model as a supervision signal in self-training. We extend the framework to a sequence prediction setting, and demonstrate its applicability to unsupervised domain adaptation. PTST achieves F1 score of 79.6{\%} on the official test set, with the precision of 90.1{\%}, the highest out of 14 submissions."
}