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.

本研究提出了一种无监督域自适应方法，采用Fine Tuning和Adversarial Label Correction，将目标领域的表示从源领域传递到靠近源领域，以此最小化目标领域实例和源实例之间的距离，并在多个目标QA数据集上得出了性能提升。

DomainInv:对于QA领域适应性的领域不变微调和对抗标签校正