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

arXiv: https://arxiv.org/abs/2104.12741 · HTML: https://mlsanity.com/paper/2104.12741/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets](https://mlsanity.com/paper/2008.02637/) (2020)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019)
- [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](https://mlsanity.com/paper/1912.09723/) (2019)

## Built on by (most cited first, 30 of 39)

- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [Semantic Answer Similarity for Evaluating Question Answering Models](https://mlsanity.com/paper/2108.06130/) (2021)
- [Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks](https://mlsanity.com/paper/2406.13469/) (2024)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings](https://mlsanity.com/paper/2402.17016/) (2024)
- [A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT](https://mlsanity.com/paper/2401.07098/) (2024)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://mlsanity.com/paper/2509.04111/) (2025)
- [A cost-benefit analysis of cross-lingual transfer methods](https://mlsanity.com/paper/2105.06813/) (2021)
- [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/2202.01764/) (2022)
- [NorQuAD: Norwegian Question Answering Dataset](https://mlsanity.com/paper/2305.01957/) (2023)
- [A Survey on non-English Question Answering Dataset](https://mlsanity.com/paper/2112.13634/) (2021)
- [Fine-tuning and aligning question answering models for complex information extraction tasks](https://mlsanity.com/paper/2309.14805/) (2023)
- [On the Impact of Cross-Domain Data on German Language Models](https://mlsanity.com/paper/2310.07321/) (2023)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models](https://mlsanity.com/paper/2505.13136/) (2025)
- [Pir'a: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean](https://mlsanity.com/paper/2202.02398/) (2022)
- [Investigating Task Arithmetic for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2505.00649/) (2025)
- [A System for Human-AI collaboration for Online Customer Support](https://mlsanity.com/paper/2301.12158/) (2023)
- [ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation](https://mlsanity.com/paper/2501.11929/) (2025)
- [A Collection of Question Answering Datasets for Norwegian](https://mlsanity.com/paper/2501.11128/) (2025)
- [Evaluating Robustness of LLMs in Question Answering on Multilingual Noisy OCR Data](https://mlsanity.com/paper/2502.16781/) (2025)
- [Evaluation of Semantic Answer Similarity Metrics](https://mlsanity.com/paper/2206.12664/) (2022)
- [AmQA: Amharic Question Answering Dataset](https://mlsanity.com/paper/2303.03290/) (2023)
- [German FinBERT: A German Pre-trained Language Model](https://mlsanity.com/paper/2311.08793/) (2023)
- [OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context](https://mlsanity.com/paper/2407.15736/) (2024)
- [Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension](https://mlsanity.com/paper/2303.13355/) (2023)
- [Building Efficient and Effective OpenQA Systems for Low-Resource Languages](https://mlsanity.com/paper/2401.03590/) (2024)
- [Multilingual De-Duplication Strategies: Applying scalable similarity search with monolingual & multilingual embedding models](https://mlsanity.com/paper/2406.13695/) (2024)

## Abstract (arXiv)

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.