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.

本文通过比较跨语言转移、翻译方法和无数据训练的三种方法，在法语和德语的临床领域中执行医学命名实体识别，并在医学 NER 测试集上进行了广泛实验，表明翻译方法可以实现与跨语言转移相似的性能，但需要更加认真地设计。

多语言临床命名实体识别：翻译还是跨语言转移？

Multilingual Clinical NER: Translation or Cross-lingual Transfer?

We consider zero-shot cross-lingual transfer in legal topic classification
using the recent MultiEURLEX dataset. Since the original dataset contains
parallel documents, which is unrealistic for zero-shot cross-lingual transfer,
we develop a new version of the dataset without parallel documents. We use it
to show that translation-based methods vastly outperform cross-lingual
fine-tuning of multilingually pre-trained models, the best previous zero-shot
transfer method for MultiEURLEX. We also develop a bilingual teacher-student
zero-shot transfer approach, which exploits additional unlabeled documents of
the target language and performs better than a model fine-tuned directly on
labeled target language documents.

本文讨论使用 MultiEURLEX 数据集进行零样本跨语言转移的法律主题分类，介绍了新版本的数据集和使用基于翻译的方法相对于之前最佳的跨语言预训练模型调参的方法的改进，同时提出了一种基于双语教师 - 学生的零样本转移方法。