  The advent of multilingual language models has generated a resurgence of
interest in cross-lingual information retrieval (CLIR), which is the task of
searching documents in one language with queries from another. However, the
rapid pace of progress has led to a confusing panoply of methods and
reproducibility has lagged behind the state of the art. In this context, our
work makes two important contributions: First, we provide a conceptual
framework for organizing different approaches to cross-lingual retrieval using
multi-stage architectures for mono-lingual retrieval as a scaffold. Second, we
implement simple yet effective reproducible baselines in the Anserini and
Pyserini IR toolkits for test collections from the TREC 2022 NeuCLIR Track, in
Persian, Russian, and Chinese. Our efforts are built on a collaboration of the
two teams that submitted the most effective runs to the TREC evaluation. These
contributions provide a firm foundation for future advances.
