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arXiv · 2104.12741

GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval

Timo Möller·Julian Risch·Malte Pietsch
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Abstract

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.

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Timo Möller, Julian Risch, Malte Pietsch. 2021-04-26. GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval. https://arxiv.org/abs/2104.12741

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