Adversarial Domain Adaptation for Machine Reading Comprehension

IJCNLP 2019 Huazheng WangZhe GanXiaodong LiuJingjing LiuJianfeng GaoHongning Wang

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... (read more)

PDF Abstract IJCNLP 2019 PDF IJCNLP 2019 Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper