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In this work, we present the first corpus for German Adverse Drug Reaction(ADR) detection in patient-generated content. The data consists of 4,169 binaryannotated documents from a German patient forum, where users talk about healthissues and get advice from medical doctors. As is common in social media datain this domain, the class labels of the corpus are very imbalanced. This and ahigh topic imbalance make it a very challenging dataset, since often, the samesymptom can have several causes and is not always related to a medicationintake. We aim to encourage further multi-lingual efforts in the domain of ADRdetection and provide preliminary experiments for binary classification usingdifferent methods of zero- and few-shot learning based on a multi-lingualmodel. When fine-tuning XLM-RoBERTa first on English patient forum data andthen on the new German data, we achieve an F1-score of 37.52 for the positiveclass. We make the dataset and models publicly available for the community.

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective