  Existing Question Answering (QA) systems limited by the capability of
answering questions from unseen domain or any out-of-domain distributions
making them less reliable for deployment to real scenarios. Most importantly
all the existing QA domain adaptation methods are either based on generating
synthetic data or pseudo labeling the target domain data. The domain adaptation
methods based on synthetic data and pseudo labeling suffers either from the
requirement of computational resources or an extra overhead of carefully
selecting the confidence threshold to separate the noisy examples from being in
the training dataset. In this paper, we propose the unsupervised domain
adaptation for unlabeled target domain by transferring the target
representation near to source domain while still using the supervision from
source domain. Towards that we proposed the idea of domain invariant fine
tuning along with adversarial label correction to identify the target instances
which lie far apart from the source domain, so that the feature encoder can be
learnt to minimize the distance between such target instances and source
instances class wisely, removing the possibility of learning the features of
target domain which are still near to source support but are ambiguous.
Evaluation of our QA domain adaptation method namely, DomainInv on multiple
target QA dataset reveal the performance improvement over the strongest
baseline.
