  Various techniques have been developed in recent years to improve dense
retrieval (DR), such as unsupervised contrastive learning and pseudo-query
generation. Existing DRs, however, often suffer from effectiveness tradeoffs
between supervised and zero-shot retrieval, which some argue was due to the
limited model capacity. We contradict this hypothesis and show that a
generalizable DR can be trained to achieve high accuracy in both supervised and
zero-shot retrieval without increasing model size. In particular, we
systematically examine the contrastive learning of DRs, under the framework of
Data Augmentation (DA). Our study shows that common DA practices such as query
augmentation with generative models and pseudo-relevance label creation using a
cross-encoder, are often inefficient and sub-optimal. We hence propose a new DA
approach with diverse queries and sources of supervision to progressively train
a generalizable DR. As a result, DRAGON, our dense retriever trained with
diverse augmentation, is the first BERT-base-sized DR to achieve
state-of-the-art effectiveness in both supervised and zero-shot evaluations and
even competes with models using more complex late interaction (ColBERTv2 and
SPLADE++).
