MultiTACRED is a multilingual version of the large-scale [TAC Relation Extraction Dataset](https://nlp.stanford.edu/projects/tacred). It covers 12 typologically diverse languages from 9 language families, and was created by machine-translating the instances of the original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's data collection and annotation process, see the [Stanford paper](https://aclanthology.org/D17-1004/). Translations are syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish, Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.
The dataset will be released via the LDC (link will follow).
Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details. You can find the Github repo containing the translation and experiment code here [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED).
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. More info: https://aclanthology.org/2022.lrec-1.388/