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.
@article{arxiv.2308.08827,
title = {Factuality Detection using Machine Translation -- a Use Case for German Clinical Text},
author = {Mohammed Bin Sumait and Aleksandra Gabryszak and Leonhard Hennig and Roland Roller},
journal= {arXiv preprint arXiv:2308.08827},
year = {2023}
}