@article{Bai2023, 
author = {Gui-Rong Bai and Qing-Bin Liu and Shi-Zhu He and Kang Liu and Jun Zhao},
title = {Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision},
year = {2023},
journal = {Journal of Computer Science and Technology},
volume = {38},
number = {6},
pages = {1237-1249},
keywords = {unsupervised domain adaptation, sentence matching, self-supervision},
url = {https://www.sciopen.com/article/10.1007/s11390-022-1479-0},
doi = {10.1007/s11390-022-1479-0},
abstract = {Although neural approaches have yielded state-of-the-art results in the sentence matching task, their performance inevitably drops dramatically when applied to unseen domains. To tackle this cross-domain challenge, we address unsupervised domain adaptation on sentence matching, in which the goal is to have good performance on a target domain with only unlabeled target domain data as well as labeled source domain data. Specifically, we propose to perform self-supervised tasks to achieve it. Different from previous unsupervised domain adaptation methods, self-supervision can not only flexibly suit the characteristics of sentence matching with a special design, but also be much easier to optimize. When training, each self-supervised task is performed on both domains simultaneously in an easy-to-hard curriculum, which gradually brings the two domains closer together along the direction relevant to the task. As a result, the classifier trained on the source domain is able to generalize to the unlabeled target domain. In total, we present three types of self-supervised tasks and the results demonstrate their superiority. In addition, we further study the performance of different usages of self-supervised tasks, which would inspire how to effectively utilize self-supervision for cross-domain scenarios.}
}