BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation

Weikang Wang, Yi Wu, Yixiang Liu, Pengyuan Liu


Abstract
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such assumption is rarely plausible in the real-world and may causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. SemEval-2021 task 10 focuses on these issues. We participate in the task and propose novel frameworks based on self-training method. In our systems, two different frameworks are designed to solve text classification and sequence labeling. These approaches are tested to be effective which ranks the third among all system in subtask A, and ranks the first among all system in subtask B.
Anthology ID:
2021.semeval-1.43
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:
357–363
Language:
URL:
https://aclanthology.org/2021.semeval-1.43
DOI:
10.18653/v1/2021.semeval-1.43
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.43.pdf