In this paper, we focus on unsupervised domain adaptation for Machine Reading
Comprehension (MRC), where the source domain has a large amount of labeled
data, while only unlabeled passages are available in the target domain. To this
end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where
($i$) pseudo questions are first generated for unlabeled passages in the target
domain, and then ($ii$) a domain classifier is incorporated into an MRC model
to predict which domain a given passage-question pair comes from. The
classifier and the passage-question encoder are jointly trained using
adversarial learning to enforce domain-invariant representation learning.
Comprehensive evaluations demonstrate that our approach ($i$) is generalizable
to different MRC models and datasets, ($ii$) can be combined with pre-trained
large-scale language models (such as ELMo and BERT), and ($iii$) can be
extended to semi-supervised learning.

本研究提出了一种针对无监督域自适应机器阅读理解的方法，其中源域有大量标记数据，而目标域只有未标记数据。该方法使用敌对领域自适应框架（AdaMRC），生成虚拟问题并结合领域分类器进行压缩编码，经过对抗学习实现域不变表示学习，具有普适性、可以与预训练的大规模语言模型相结合，并可扩展到半监督学习。