  Most previous unsupervised domain adaptation (UDA) methods for question
answering(QA) require access to source domain data while fine-tuning the model
for the target domain. Source domain data may, however, contain sensitive
information and may be restricted. In this study, we investigate a more
challenging setting, source-free UDA, in which we have only the pretrained
source model and target domain data, without access to source domain data. We
propose a novel self-training approach to QA models that integrates a unique
mask module for domain adaptation. The mask is auto-adjusted to extract key
domain knowledge while trained on the source domain. To maintain previously
learned domain knowledge, certain mask weights are frozen during adaptation,
while other weights are adjusted to mitigate domain shifts with pseudo-labeled
samples generated in the target domain. %As part of the self-training process,
we generate pseudo-labeled samples in the target domain based on models trained
in the source domain. Our empirical results on four benchmark datasets suggest
that our approach significantly enhances the performance of pretrained QA
models on the target domain, and even outperforms models that have access to
the source data during adaptation.
