  Recent advance in Dense Retrieval (DR) techniques has significantly improved
the effectiveness of first-stage retrieval. Trained with large-scale supervised
data, DR models can encode queries and documents into a low-dimensional dense
space and conduct effective semantic matching. However, previous studies have
shown that the effectiveness of DR models would drop by a large margin when the
trained DR models are adopted in a target domain that is different from the
domain of the labeled data. One of the possible reasons is that the DR model
has never seen the target corpus and thus might be incapable of mitigating the
difference between the training and target domains. In practice, unfortunately,
training a DR model for each target domain to avoid domain shift is often a
difficult task as it requires additional time, storage, and domain-specific
data labeling, which are not always available. To address this problem, in this
paper, we propose a novel DR framework named Disentangled Dense Retrieval (DDR)
to support effective and flexible domain adaptation for DR models. DDR consists
of a Relevance Estimation Module (REM) for modeling domain-invariant matching
patterns and several Domain Adaption Modules (DAMs) for modeling
domain-specific features of multiple target corpora. By making the REM and DAMs
disentangled, DDR enables a flexible training paradigm in which REM is trained
with supervision once and DAMs are trained with unsupervised data.
Comprehensive experiments in different domains and languages show that DDR
significantly improves ranking performance compared to strong DR baselines and
substantially outperforms traditional retrieval methods in most scenarios.
