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Dense retrieval (DR) methods conduct text retrieval by first encoding textsin the embedding space and then matching them by nearest neighbor search. Thisrequires strong locality properties from the representation space, i.e, theclose allocations of each small group of relevant texts, which are hard togeneralize to domains without sufficient training data. In this paper, we aimto improve the generalization ability of DR models from source training domainswith rich supervision signals to target domains without any relevant labels, inthe zero-shot setting. To achieve that, we propose Momentum adversarial DomainInvariant Representation learning (MoDIR), which introduces a momentum methodin the DR training process to train a domain classifier distinguishing sourceversus target, and then adversarially updates the DR encoder to learn domaininvariant representations. Our experiments show that MoDIR robustly outperformsits baselines on 10+ ranking datasets from the BEIR benchmark in the zero-shotsetup, with more than 10% relative gains on datasets with enough sensitivityfor DR models' evaluation. Source code of this paper will be released.

Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations