  In Information Retrieval, and more generally in Natural Language Processing,
adapting models to specific domains is conducted through fine-tuning. Despite
the successes achieved by this method and its versatility, the need for
human-curated and labeled data makes it impractical to transfer to new tasks,
domains, and/or languages when training data doesn't exist. Using the model
without training (zero-shot) is another option that however suffers an
effectiveness cost, especially in the case of first-stage retrievers. Numerous
research directions have emerged to tackle these issues, most of them in the
context of adapting to a task or a language. However, the literature is scarcer
for domain (or topic) adaptation. In this paper, we address this issue of
cross-topic discrepancy for a sparse first-stage retriever by transposing a
method initially designed for language adaptation. By leveraging pre-training
on the target data to learn domain-specific knowledge, this technique
alleviates the need for annotated data and expands the scope of domain
adaptation. Despite their relatively good generalization ability, we show that
even sparse retrievers can benefit from our simple domain adaptation method.
