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

Factuality Detection using Machine Translation -- a Use Case for German Clinical Text