2021 SEMEVAL SemEval 2021

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

Abstract

AbstractThis 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio