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Objective: To improve performance of medical entity normalization across manylanguages, especially when fewer language resources are available compared toEnglish.  Materials and Methods: We introduce xMEN, a modular system for cross-lingualmedical entity normalization, which performs well in both low- andhigh-resource scenarios. When synonyms in the target language are scarce for agiven terminology, we leverage English aliases via cross-lingual candidategeneration. For candidate ranking, we incorporate a trainable cross-encodermodel if annotations for the target task are available. We also evaluatecross-encoders trained in a weakly supervised manner based onmachine-translated datasets from a high resource domain. Our system is publiclyavailable as an extensible Python toolkit.  Results: xMEN improves the state-of-the-art performance across a wide rangeof multilingual benchmark datasets. Weakly supervised cross-encoders areeffective when no training data is available for the target task. Through thecompatibility of xMEN with the BigBIO framework, it can be easily used withexisting and prospective datasets.  Discussion: Our experiments show the importance of balancing the output ofgeneral-purpose candidate generators with subsequent trainable re-rankers,which we achieve through a rank regularization term in the loss function of thecross-encoder. However, error analysis reveals that multi-word expressions andother complex entities are still challenging.  Conclusion: xMEN exhibits strong performance for medical entity normalizationin multiple languages, even when no labeled data and few terminology aliasesfor the target language are available. Its configuration system and evaluationmodules enable reproducible benchmarks. Models and code are available online atthe following URL: https://github.com/hpi-dhc/xmen

xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization