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

Timo Möller, Julian Risch, Malte Pietsch


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 one of the first non-English DPR models.
Anthology ID:
2021.mrqa-1.4
Volume:
Proceedings of the 3rd Workshop on Machine Reading for Question Answering
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
MRQA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
42–50
Language:
URL:
https://aclanthology.org/2021.mrqa-1.4
DOI:
10.18653/v1/2021.mrqa-1.4
Bibkey:
Cite (ACL):
Timo Möller, Julian Risch, and Malte Pietsch. 2021. GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval. In Proceedings of the 3rd Workshop on Machine Reading for Question Answering, pages 42–50, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval (Möller et al., MRQA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.mrqa-1.4.pdf
Data
GermanDPRGermanQuADMLQANatural QuestionsXQuAD