@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging
%A Kurniawan, Kemal
%A Frermann, Lea
%A Schulz, Philip
%A Cohn, Trevor
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F kurniawan-etal-2021-ptst
%X 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.
%R 10.18653/v1/2021.semeval-1.54
%U https://aclanthology.org/2021.semeval-1.54
%U https://doi.org/10.18653/v1/2021.semeval-1.54
%P 445-451
Markdown (Informal)
[PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging](https://aclanthology.org/2021.semeval-1.54) (Kurniawan et al., SemEval 2021)
ACL