SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing

Egoitz Laparra, Xin Su, Yiyun Zhao, Özlem Uzuner, Timothy Miller, Steven Bethard


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).
Anthology ID:
2021.semeval-1.42
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
348–356
Language:
URL:
https://aclanthology.org/2021.semeval-1.42
DOI:
10.18653/v1/2021.semeval-1.42
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.42.pdf