%0 Conference Proceedings %T SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing %A Laparra, Egoitz %A Su, Xin %A Zhao, Yiyun %A Uzuner, Özlem %A Miller, Timothy %A Bethard, Steven %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 laparra-etal-2021-semeval %X 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). %R 10.18653/v1/2021.semeval-1.42 %U https://aclanthology.org/2021.semeval-1.42 %U https://doi.org/10.18653/v1/2021.semeval-1.42 %P 348-356