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 (Kurniawan et al., 2021), 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.
CITATION STYLE
Kurniawan, K., Frermann, L., Schulz, P., & Cohn, T. (2021). PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging. In SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 445–451). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.semeval-1.54
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