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

arXiv: https://arxiv.org/abs/2308.08827 · HTML: https://mlsanity.com/paper/2308.08827/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [Multilingual Extractive Reading Comprehension by Runtime Machine Translation](https://mlsanity.com/paper/1809.03275/) (2018)
- [Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction](https://mlsanity.com/paper/2109.06798/) (2021)
- [Cross-Lingual Machine Reading Comprehension](https://mlsanity.com/paper/1909.00361/) (2019)
- [Learning Disentangled Representations of Negation and Uncertainty](https://mlsanity.com/paper/2204.00511/) (2022)
- [Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures](https://mlsanity.com/paper/1907.03227/) (2019)
- [Transferring Semantic Roles Using Translation and Syntactic Information](https://mlsanity.com/paper/1710.01411/) (2017)
- [Modality and Negation in Event Extraction](https://mlsanity.com/paper/2109.09393/) (2021)

## Abstract (arXiv)

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.