GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval
This addresses the problem of limited resources for non-English QA research, though it is incremental as it extends existing methods to a new language.
The authors tackled the lack of annotated datasets for non-English question answering by creating GermanQuAD, a dataset of 13,722 extractive question/answer pairs, and showed that a model trained on it significantly outperforms multilingual models and machine-translated data.
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.