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.

翻译：多语言语言模型的出现引发了对跨语言信息检索（CLIR）的再次关注，即使用另一种语言的查询搜索一种语言的文档。然而，进展的快速速度导致了一系列混乱的方法，因此其可重复性落后于现有技术水平。在这种情况下，我们的工作提供了两个重要的贡献：首先，我们提供了一个概念框架，以多阶段体系结构为主干，对使用不同方法进行跨语言检索进行组织。其次，在中文，俄语和波斯语的TREC 2022 NeuCLIR Track测试集中，我们在Anserini和Pyserini IR工具包中实现了简单而有效的可重复基线。我们的努力基于两个团队向TREC评估提交了最有效的运行。这些贡献为未来的进步打下了坚实的基础。