End-to-end question answering (QA) requires both information retrieval (IR)
over a large document collection and machine reading comprehension (MRC) on the
retrieved passages. Recent work has successfully trained neural IR systems
using only supervised question answering (QA) examples from open-domain
datasets. However, despite impressive performance on Wikipedia, neural IR lags
behind traditional term matching approaches such as BM25 in more specific and
specialized target domains such as COVID-19. Furthermore, given little or no
labeled data, effective adaptation of QA systems can also be challenging in
such target domains. In this work, we explore the application of synthetically
generated QA examples to improve performance on closed-domain retrieval and
MRC. We combine our neural IR and MRC systems and show significant improvements
in end-to-end QA on the CORD-19 collection over a state-of-the-art open-domain
QA baseline.

本文探讨了如何通过合成的问答数据来提高封闭领域检索和机器阅读理解的性能，实验结果表明将神经信息检索系统和机器阅读理解系统组合后，在 CORD-19 数据集上相比于基准开放领域问答系统，有显著的改进。