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Existing Question Answering (QA) systems limited by the capability ofanswering questions from unseen domain or any out-of-domain distributionsmaking them less reliable for deployment to real scenarios. Most importantlyall the existing QA domain adaptation methods are either based on generatingsynthetic data or pseudo labeling the target domain data. The domain adaptationmethods based on synthetic data and pseudo labeling suffers either from therequirement of computational resources or an extra overhead of carefullyselecting the confidence threshold to separate the noisy examples from being inthe training dataset. In this paper, we propose the unsupervised domainadaptation for unlabeled target domain by transferring the targetrepresentation near to source domain while still using the supervision fromsource domain. Towards that we proposed the idea of domain invariant finetuning along with adversarial label correction to identify the target instanceswhich lie far apart from the source domain, so that the feature encoder can belearnt to minimize the distance between such target instances and sourceinstances class wisely, removing the possibility of learning the features oftarget domain which are still near to source support but are ambiguous.Evaluation of our QA domain adaptation method namely, DomainInv on multipletarget QA dataset reveal the performance improvement over the strongestbaseline.

DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation