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Publikation

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

Mohammed Bin Sumait; Aleksandra Gabryszak; Leonhard Hennig; Roland Roller
In: Munir Georges; Aaricia Herygers; Annemarie Friedrich; Benjamin Roth (Hrsg.). Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023). Conference on Natural Language Processing (KONVENS-2023), September 18-22, Ingolstadt, Germany, Pages 85-92, Association for Computational Lingustics, 2023.

Zusammenfassung

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.

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