@inproceedings{moller-etal-2021-germanquad,
title = "{G}erman{Q}u{AD} and {G}erman{DPR}: Improving Non-{E}nglish Question Answering and Passage Retrieval",
author = {M{\"o}ller, Timo and
Risch, Julian and
Pietsch, Malte},
booktitle = "Proceedings of the 3rd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrqa-1.4",
doi = "10.18653/v1/2021.mrqa-1.4",
pages = "42--50",
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.",
}
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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 one of the first non-English DPR models.</abstract>
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%0 Conference Proceedings
%T GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval
%A Möller, Timo
%A Risch, Julian
%A Pietsch, Malte
%S Proceedings of the 3rd Workshop on Machine Reading for Question Answering
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F moller-etal-2021-germanquad
%X 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.
%R 10.18653/v1/2021.mrqa-1.4
%U https://aclanthology.org/2021.mrqa-1.4
%U https://doi.org/10.18653/v1/2021.mrqa-1.4
%P 42-50
Markdown (Informal)
[GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://aclanthology.org/2021.mrqa-1.4) (Möller et al., MRQA 2021)
ACL