Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation
Abstract
Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment. We study the domain adaption in the sequence labeling problem where the model trained on the source domain data is given. We propose two methods: Self-Adapter and Selective Classifier Training. Self-Adapter is a training method that uses sentence-level pseudo-labels filtered by the self-entropy threshold to provide supervision to the whole model. Selective Classifier Training uses token-level pseudo-labels and supervises only the classification layer of the model. The proposed methods are evaluated on data provided by SemEval-2021 task 10 and Self-Adapter achieves 2nd rank performance.- Anthology ID:
- 2021.semeval-1.55
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 452–457
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.55
- DOI:
- 10.18653/v1/2021.semeval-1.55
- Cite (ACL):
- Sangwon Yoon, Yanghoon Kim, and Kyomin Jung. 2021. Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 452–457, Online. Association for Computational Linguistics.
- Cite (Informal):
- Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation (Yoon et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.semeval-1.55.pdf