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

Mohammed Bin Sumait; Aleksandra Gabryszak; Leonhard Hennig; Roland Roller

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arXiv · 2308.08827

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

Mohammed Bin Sumait·Aleksandra Gabryszak·Leonhard Hennig·Roland Roller

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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.

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Mohammed Bin Sumait, Aleksandra Gabryszak, Leonhard Hennig, Roland Roller. 2023-08-17. Factuality Detection using Machine Translation -- a Use Case for German Clinical Text. https://arxiv.org/abs/2308.08827

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