@inproceedings{c3b3f6cfcf904b1a934f8acfb5b934e1,
title = "SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing",
abstract = "This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021. The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints. Specifically, we consider the scenario where annotations exist for a domain but cannot be shared. Instead, participants are provided with models trained on that (source) data. Participants also receive some labeled data from a new (development) domain on which to explore domain adaptation algorithms. Participants are then tested on data representing a new (target) domain. We explored this scenario with two different semantic tasks: negation detection (a text classification task) and time expression recognition (a sequence tagging task).",
author = "Egoitz Laparra and Xin Su and Yiyun Zhao and {\"O}zlem Uzuner and Miller, {Timothy A.} and Steven Bethard",
note = "Funding Information: Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Numbers R01LM012918 and R01LM010090. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 15th International Workshop on Semantic Evaluation, SemEval 2021 ; Conference date: 05-08-2021 Through 06-08-2021",
year = "2021",
language = "English (US)",
series = "SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "348--356",
editor = "Alexis Palmer and Nathan Schneider and Natalie Schluter and Guy Emerson and Aurelie Herbelot and Xiaodan Zhu",
booktitle = "SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop",
}