Question Answering (QA) has shown great success thanks to the availability of
large-scale datasets and the effectiveness of neural models. Recent research
works have attempted to extend these successes to the settings with few or no
labeled data available. In this work, we introduce two approaches to improve
unsupervised QA. First, we harvest lexically and syntactically divergent
questions from Wikipedia to automatically construct a corpus of question-answer
pairs (named as RefQA). Second, we take advantage of the QA model to extract
more appropriate answers, which iteratively refines data over RefQA. We conduct
experiments on SQuAD 1.1, and NewsQA by fine-tuning BERT without access to
manually annotated data. Our approach outperforms previous unsupervised
approaches by a large margin and is competitive with early supervised models.
We also show the effectiveness of our approach in the few-shot learning
setting.

本研究提出了两种方法以改善无监督问答：从维基百科中收集问题 - 答案对以自动构建语料库，利用 QA 模型提取更合适的答案迭代地优化 RefQA 数据。经过对 SQuAD 1.1 和 NewsQA 进行的实验证明我们的方法在无监督情况下优于之前的方法，并与早期的监督模型具有竞争力。我们还在少样本学习方面证明了我们方法的有效性。