Multilingual Clinical NER: Translation or Cross-lingual Transfer?
Xavier Fontaine Félix Gaschi Parisa Rastin Yannick Toussaint
Natural language tasks like Named Entity Recognition (NER) in the clinical
domain on non-English texts can be very time-consuming and expensive due to the
lack of annotated data. Cross-lingual transfer (CLT) is a way to circumvent
this issue thanks to the ability of multilingual large language models to be
fine-tuned on a specific task in one language and to provide high accuracy for
the same task in another language. However, other methods leveraging
translation models can be used to perform NER without annotated data in the
target language, by either translating the training set or test set. This paper
compares cross-lingual transfer with these two alternative methods, to perform
clinical NER in French and in German without any training data in those
languages. To this end, we release MedNERF a medical NER test set extracted
from French drug prescriptions and annotated with the same guidelines as an
English dataset. Through extensive experiments on this dataset and on a German
medical dataset (Frei and Kramer, 2021), we show that translation-based methods
can achieve similar performance to CLT but require more care in their design.
And while they can take advantage of monolingual clinical language models,
those do not guarantee better results than large general-purpose multilingual
models, whether with cross-lingual transfer or translation.