  State-of-the-art neural rankers pre-trained on large task-specific training
data such as MS-MARCO, have been shown to exhibit strong performance on various
ranking tasks without domain adaptation, also called zero-shot. However,
zero-shot neural ranking may be sub-optimal, as it does not take advantage of
the target domain information. Unfortunately, acquiring sufficiently large and
high quality target training data to improve a modern neural ranker can be
costly and time-consuming. To address this problem, we propose a new approach
to unsupervised domain adaptation for ranking, DUQGen, which addresses a
critical gap in prior literature, namely how to automatically generate both
effective and diverse synthetic training data to fine tune a modern neural
ranker for a new domain. Specifically, DUQGen produces a more effective
representation of the target domain by identifying clusters of similar
documents; and generates a more diverse training dataset by probabilistic
sampling over the resulting document clusters. Our extensive experiments, over
the standard BEIR collection, demonstrate that DUQGen consistently outperforms
all zero-shot baselines and substantially outperforms the SOTA baselines on 16
out of 18 datasets, for an average of 4% relative improvement across all
datasets. We complement our results with a thorough analysis for more in-depth
understanding of the proposed method's performance and to identify promising
areas for further improvements.
