  This study tackles unsupervised domain adaptation of reading comprehension
(UDARC). Reading comprehension (RC) is a task to learn the capability for
question answering with textual sources. State-of-the-art models on RC still do
not have general linguistic intelligence; i.e., their accuracy worsens for
out-domain datasets that are not used in the training. We hypothesize that this
discrepancy is caused by a lack of the language modeling (LM) capability for
the out-domain. The UDARC task allows models to use supervised RC training data
in the source domain and only unlabeled passages in the target domain. To solve
the UDARC problem, we provide two domain adaptation models. The first one
learns the out-domain LM and in-domain RC task sequentially. The second one is
the proposed model that uses a multi-task learning approach of LM and RC. The
models can retain both the RC capability acquired from the supervised data in
the source domain and the LM capability from the unlabeled data in the target
domain. We evaluated the models on UDARC with five datasets in different
domains. The models outperformed the model without domain adaptation. In
particular, the proposed model yielded an improvement of 4.3/4.2 points in
EM/F1 in an unseen biomedical domain.
