  Dense retrievers have made significant strides in text retrieval and
open-domain question answering, even though most achievements were made
possible only with large amounts of human supervision. In this work, we aim to
develop unsupervised methods by proposing two methods that create pseudo
query-document pairs and train dense retrieval models in an annotation-free and
scalable manner: query extraction and transferred query generation. The former
method produces pseudo queries by selecting salient spans from the original
document. The latter utilizes generation models trained for other NLP tasks
(e.g., summarization) to produce pseudo queries. Extensive experiments show
that models trained with the proposed augmentation methods can perform
comparably well (or better) to multiple strong baselines. Combining those
strategies leads to further improvements, achieving the state-of-the-art
performance of unsupervised dense retrieval on both BEIR and ODQA datasets.
