AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation

Understand

Dense retrievers have made significant strides in text retrieval and open-domain question answering.

• However, most of these achievements have relied heavily on extensive human-annotated supervision.
• In this study, we aim to develop unsupervised methods for improving dense retrieval models.
• We propose two approaches that enable annotation-free and scalable training by creating pseudo querydocument pairs: query extraction and transferred query generation.