  A major challenge of research on non-English machine reading for question
answering (QA) is the lack of annotated datasets. In this paper, we present
GermanQuAD, a dataset of 13,722 extractive question/answer pairs. To improve
the reproducibility of the dataset creation approach and foster QA research on
other languages, we summarize lessons learned and evaluate reformulation of
question/answer pairs as a way to speed up the annotation process. An
extractive QA model trained on GermanQuAD significantly outperforms
multilingual models and also shows that machine-translated training data cannot
fully substitute hand-annotated training data in the target language. Finally,
we demonstrate the wide range of applications of GermanQuAD by adapting it to
GermanDPR, a training dataset for dense passage retrieval (DPR), and train and
evaluate the first non-English DPR model.
