Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed. In most cases, a sufficient number of examples is necessary to handle such phenomena in a supervised machine learning setting. However, as clinical text might contain sensitive information, data cannot be easily shared. In the context of factuality detection, this work presents a simple solution using machine translation to translate English data to German to train a transformer-based factuality detection model.

翻译：事实性在自动处理临床文本时可能发挥重要作用，因为特定症状是否明确不存在、可能存在、未提及或已确认，会产生显著差异。在大多数情况下，处理这类监督式机器学习场景中的现象需要充足的样本量。然而，由于临床文本可能包含敏感信息，数据无法轻易共享。本研究针对事实性检测任务，提出一种利用机器翻译将英语数据转换为德语的简单解决方案，用于训练基于Transformer架构的事实性检测模型。