多语言临床NER：翻译还是跨语言迁移？
Multilingual Clinical NER: Translation or Cross-lingual Transfer?
AI总结：
针对法语和德语临床NER缺少标注数据的问题，本文比较跨语言迁移与翻译训练集/测试集两类方法，并发布法语MedNERF测试集，证明翻译法可达相近性能但设计需更谨慎。
摘要：
由于缺乏标注数据，在非英文文本上开展临床领域的命名实体识别（NER）等自然语言任务可能非常耗时且昂贵。跨语言迁移（CLT）是规避这一问题的一种方式：多语言大型语言模型能够在一种语言上针对特定任务进行微调，并在另一种语言的同一任务上提供较高准确率。然而，也可以使用其他利用翻译模型的方法，在目标语言没有标注数据的情况下执行NER，具体做法是翻译训练集或测试集。本文将跨语言迁移与这两种替代方法进行比较，以在法语和德语中没有任何训练数据的情况下执行临床NER。为此，我们发布了MedNERF，这是一个从法语药物处方中提取的医学NER测试集，并按照与某一英文数据集相同的指南进行标注。通过在该数据集以及一个德语医学数据集（Frei and Kramer, 2021）上进行大量实验，我们表明，基于翻译的方法可以取得与CLT相近的性能，但在设计上需要更加谨慎。此外，尽管这些方法能够利用单语言临床语言模型，但无论是采用跨语言迁移还是翻译，这类模型都不能保证比大型通用多语言模型取得更好的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
