@inproceedings{laparra-etal-2021-semeval,
title = "{S}em{E}val-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing",
author = {Laparra, Egoitz and
Su, Xin and
Zhao, Yiyun and
Uzuner, {\"O}zlem and
Miller, Timothy and
Bethard, Steven},
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-1.42/",
doi = "10.18653/v1/2021.semeval-1.42",
pages = "348--356",
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)."
}
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%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
Markdown (Informal)
[SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing](https://aclanthology.org/2021.semeval-1.42/) (Laparra et al., SemEval 2021)
ACL