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
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- 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
- Cite (ACL):
- Egoitz Laparra, Xin Su, Yiyun Zhao, Özlem Uzuner, Timothy Miller, and Steven Bethard. 2021. SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 348–356, Online. Association for Computational Linguistics.
- Cite (Informal):
- SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing (Laparra et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.42.pdf
- Code
- machine-learning-for-medical-language/source-free-domain-adaptation