Dense Retrieval Adaptation using Target Domain Description
Helia Hashemi Yong Zhuang Sachith Sri Ram Kothur Srivas Prasad Edgar Meij W. Bruce Croft
In information retrieval (IR), domain adaptation is the process of adapting a
retrieval model to a new domain whose data distribution is different from the
source domain. Existing methods in this area focus on unsupervised domain
adaptation where they have access to the target document collection or
supervised (often few-shot) domain adaptation where they additionally have
access to (limited) labeled data in the target domain. There also exists
research on improving zero-shot performance of retrieval models with no
adaptation. This paper introduces a new category of domain adaptation in IR
that is as-yet unexplored. Here, similar to the zero-shot setting, we assume
the retrieval model does not have access to the target document collection. In
contrast, it does have access to a brief textual description that explains the
target domain. We define a taxonomy of domain attributes in retrieval tasks to
understand different properties of a source domain that can be adapted to a
target domain. We introduce a novel automatic data construction pipeline that
produces a synthetic document collection, query set, and pseudo relevance
labels, given a textual domain description. Extensive experiments on five
diverse target domains show that adapting dense retrieval models using the
constructed synthetic data leads to effective retrieval performance on the
target domain.