DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation
This addresses the reliability issue for deploying QA systems to real-world scenarios, though it is incremental as it builds on existing domain adaptation methods.
The paper tackles the problem of QA systems struggling with unseen domains by proposing an unsupervised domain adaptation method that transfers target representations closer to the source domain while using source supervision, resulting in performance improvements over strong baselines on multiple target QA datasets.
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.