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

Source: [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](<https://www.sota2.com/research/paper/68fa69d41e209131cea24743>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:09:38.543Z

Authors: Timo M\\"oller, Julian Risch, Malte Pietsch

Published: 2021-04-26T17:34:31.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.12741>)

[PDF](<https://arxiv.org/pdf/2104.12741.pdf>)