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 binary
annotated documents from a German patient forum, where users talk about health
issues and get advice from medical doctors. As is common in social media data
in this domain, the class labels of the corpus are very imbalanced. This and a
high topic imbalance make it a very challenging dataset, since often, the same
symptom can have several causes and is not always related to a medication
intake. We aim to encourage further multi-lingual efforts in the domain of ADR
detection and provide preliminary experiments for binary classification using
different methods of zero- and few-shot learning based on a multi-lingual
model. When fine-tuning XLM-RoBERTa first on English patient forum data and
then on the new German data, we achieve an F1-score of 37.52 for the positive
class. We make the dataset and models publicly available for the community.

本文首次提出了一个针对德国患者提供的 Adverse Drug Reaction 侦测语料库，包含了德语患者论坛中 4169 个二进制标注文件，并呈现了这个社交媒体数据的课程和主题的不平衡性，通过使用 XLM-RoBERTa 在英语和德语患者论坛数据之前进行优化，最终实现了一个 37.52 的二元分类 F1 - 得分。