Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision

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 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-su-pervised tasks to achieve it.Different 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 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-super-vised 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.