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Unsupervised Domain Adaptation on sentence matching Through Self-Supervision

Journal of Computer Science & Technology 2023年第6期38卷 1237-1249页

作者：白桂荣刘庆斌何世柱刘康赵军 National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Although neural approaches have yielded state-of-the-art results in the sentence matching task,their perfor-mance inevitably drops dramatically when applied to unseen *** 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 ***,we propose to perform self-su-pervised tasks to achieve *** from previous unsupervised domain adaptation methods,self-supervision can not on-ly flexibly suit the characteristics of sentence matching with a special design,but also be much easier to *** 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 *** a result,the classifier trained on the source domain is able to generalize to the unlabeled target *** total,we present three types of self-super-vised tasks and the results demonstrate their *** 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.

关键词： unsupervised domain adaptation sentence matching self-supervision

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