PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging

Kemal Kurniawan, Lea Frermann, Philip Schulz, Trevor Cohn

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.
Anthology ID:
2021.semeval-1.54
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
445–451
Language:
URL:
https://aclanthology.org/2021.semeval-1.54
DOI:
10.18653/v1/2021.semeval-1.54
Bibkey:
Cite (ACL):
Kemal Kurniawan, Lea Frermann, Philip Schulz, and Trevor Cohn. 2021. PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 445–451, Online. Association for Computational Linguistics.
Cite (Informal):
PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging (Kurniawan et al., SemEval 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.54.pdf
PDF Search