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.

翻译：像命名实体识别（NER）这样的自然语言任务，在临床领域处理非英语文本时，由于缺乏标注数据，往往非常耗时且成本高昂。跨语言迁移（CLT）可通过多语言大语言模型的能力绕过这一问题——该模型能在一种语言上针对特定任务进行微调，并为另一种语言的相同任务提供高准确率。然而，利用翻译模型的其他方法也可在不使用目标语言标注数据的情况下执行NER，方法包括翻译训练集或测试集。本文比较了跨语言迁移与这两种替代方法，以在法语和德语上执行临床NER，且无需这些语言的训练数据。为此，我们发布了MedNERF——一个从法语药物处方中提取的医学NER测试集，并按照与英文数据集相同的标注指南进行标注。通过在该数据集及德语医学数据集上的大量实验，我们发现基于翻译的方法能达到与CLT相近的性能，但需要更精心的设计。此外，尽管这些方法可利用单语临床语言模型，但无论采用跨语言迁移还是翻译策略，这些模型都未必能比大型通用多语言模型带来更优的结果。