  Question answering (QA) has recently shown impressive results for answering
questions from customized domains. Yet, a common challenge is to adapt QA
models to an unseen target domain. In this paper, we propose a novel
self-supervised framework called QADA for QA domain adaptation. QADA introduces
a novel data augmentation pipeline used to augment training QA samples.
Different from existing methods, we enrich the samples via hidden space
augmentation. For questions, we introduce multi-hop synonyms and sample
augmented token embeddings with Dirichlet distributions. For contexts, we
develop an augmentation method which learns to drop context spans via a custom
attentive sampling strategy. Additionally, contrastive learning is integrated
in the proposed self-supervised adaptation framework QADA. Unlike existing
approaches, we generate pseudo labels and propose to train the model via a
novel attention-based contrastive adaptation method. The attention weights are
used to build informative features for discrepancy estimation that helps the QA
model separate answers and generalize across source and target domains. To the
best of our knowledge, our work is the first to leverage hidden space
augmentation and attention-based contrastive adaptation for self-supervised
domain adaptation in QA. Our evaluation shows that QADA achieves considerable
improvements on multiple target datasets over state-of-the-art baselines in QA
domain adaptation.
