xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization
Florian Borchert Ignacio Llorca Roland Roller Bert Arnrich Matthieu-P. Schapranow
Objective: To improve performance of medical entity normalization across many
languages, especially when fewer language resources are available compared to
English.
  Materials and Methods: We introduce xMEN, a modular system for cross-lingual
medical entity normalization, which performs well in both low- and
high-resource scenarios. When synonyms in the target language are scarce for a
given terminology, we leverage English aliases via cross-lingual candidate
generation. For candidate ranking, we incorporate a trainable cross-encoder
model if annotations for the target task are available. We also evaluate
cross-encoders trained in a weakly supervised manner based on
machine-translated datasets from a high resource domain. Our system is publicly
available as an extensible Python toolkit.
  Results: xMEN improves the state-of-the-art performance across a wide range
of multilingual benchmark datasets. Weakly supervised cross-encoders are
effective when no training data is available for the target task. Through the
compatibility of xMEN with the BigBIO framework, it can be easily used with
existing and prospective datasets.
  Discussion: Our experiments show the importance of balancing the output of
general-purpose candidate generators with subsequent trainable re-rankers,
which we achieve through a rank regularization term in the loss function of the
cross-encoder. However, error analysis reveals that multi-word expressions and
other complex entities are still challenging.
  Conclusion: xMEN exhibits strong performance for medical entity normalization
in multiple languages, even when no labeled data and few terminology aliases
for the target language are available. Its configuration system and evaluation
modules enable reproducible benchmarks. Models and code are available online at
the following URL: https://github.com/hpi-dhc/xmen