Factuality Detection using Machine Translation -- a Use Case for German
  Clinical Text
Mohammed Bin Sumait Aleksandra Gabryszak Leonhard Hennig Roland Roller
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.