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.

本文介绍了一个非英语机器阅读问答研究的主要挑战：缺乏注释数据集。我们随后提出一个德语 QA 数据集（GermanQuAD），总结经验教训，并评估 QA 问答对重组的效果，以加速注释过程。通过 GermanQuAD 数据训练，提出的 QA 模型明显优于多语言模型。最后，将数据集适应于 DPR 语言，训练了第一款非英语 DPR 模型。