  Question answering (QA) has demonstrated impressive progress in answering
questions from customized domains. Nevertheless, domain adaptation remains one
of the most elusive challenges for QA systems, especially when QA systems are
trained in a source domain but deployed in a different target domain. In this
work, we investigate the potential benefits of question classification for QA
domain adaptation. We propose a novel framework: Question Classification for
Question Answering (QC4QA). Specifically, a question classifier is adopted to
assign question classes to both the source and target data. Then, we perform
joint training in a self-supervised fashion via pseudo-labeling. For
optimization, inter-domain discrepancy between the source and target domain is
reduced via maximum mean discrepancy (MMD) distance. We additionally minimize
intra-class discrepancy among QA samples of the same question class for
fine-grained adaptation performance. To the best of our knowledge, this is the
first work in QA domain adaptation to leverage question classification with
self-supervised adaptation. We demonstrate the effectiveness of the proposed
QC4QA with consistent improvements against the state-of-the-art baselines on
multiple datasets.
