# NorQuAD: Norwegian Question Answering Dataset

arXiv: https://arxiv.org/abs/2305.01957 · HTML: https://mlsanity.com/paper/2305.01957/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 3 May 2023.

## 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)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [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)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](https://mlsanity.com/paper/1912.09723/) (2019)
- [Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model](https://mlsanity.com/paper/2104.09617/) (2021)
- [Large-Scale Contextualised Language Modelling for Norwegian](https://mlsanity.com/paper/2104.06546/) (2021)
- [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/2202.01764/) (2022)

## Built on by (most cited first, 7 of 7)

- [NorBench -- A Benchmark for Norwegian Language Models](https://mlsanity.com/paper/2305.03880/) (2023)
- [An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://mlsanity.com/paper/2503.10267/) (2025)
- [Small Languages, Big Models: A Study of Continual Training on Languages of Norway](https://mlsanity.com/paper/2412.06484/) (2024)
- [The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective](https://mlsanity.com/paper/2412.09460/) (2024)
- [NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark](https://mlsanity.com/paper/2504.07749/) (2025)
- [A Collection of Question Answering Datasets for Norwegian](https://mlsanity.com/paper/2501.11128/) (2025)
- [Fluent Alignment with Disfluent Judges: Post-training for Lower-resource Languages](https://mlsanity.com/paper/2512.08777/) (2025)

## Abstract (arXiv)

In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegian monolingual language models on the dataset and compare them against human performance. The dataset will be made freely available.