  Recent studies have demonstrated that the ability of dense retrieval models
to generalize to target domains with different distributions is limited, which
contrasts with the results obtained with interaction-based models. Prior
attempts to mitigate this challenge involved leveraging adversarial learning
and query generation approaches, but both approaches nevertheless resulted in
limited improvements. In this paper, we propose to combine the query-generation
approach with a self-supervision approach in which pseudo-relevance labels are
automatically generated on the target domain. To accomplish this, a T5-3B model
is utilized for pseudo-positive labeling, and meticulous hard negatives are
chosen. We also apply this strategy on conversational dense retrieval model for
conversational search. A similar pseudo-labeling approach is used, but with the
addition of a query-rewriting module to rewrite conversational queries for
subsequent labeling. This proposed approach enables a model's domain adaptation
with real queries and documents from the target dataset. Experiments on
standard dense retrieval and conversational dense retrieval models both
demonstrate improvements on baseline models when they are fine-tuned on the
pseudo-relevance labeled data.
