Abstract
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.
Type
Publication
Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)
Aleksandra Gabryszak
PhD Candidate
Leonhard Hennig
Senior Researcher
