# KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1909.07005 · HTML: https://mlsanity.com/paper/1909.07005/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 30 of 43)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://mlsanity.com/paper/2402.11548/) (2024)
- [On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model](https://mlsanity.com/paper/2204.13509/) (2022)
- [What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers](https://mlsanity.com/paper/2109.04650/) (2021)
- [CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean](https://mlsanity.com/paper/2403.06412/) (2024)
- [Language Modelling with Pixels](https://mlsanity.com/paper/2207.06991/) (2022)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [CaLMQA: Exploring culturally specific long-form question answering across 23 languages](https://mlsanity.com/paper/2406.17761/) (2024)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources](https://mlsanity.com/paper/2211.15649/) (2022)
- [An Efficient Multilingual Language Model Compression through Vocabulary Trimming](https://mlsanity.com/paper/2305.15020/) (2023)
- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [ParaShoot: A Hebrew Question Answering Dataset](https://mlsanity.com/paper/2109.11314/) (2021)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [Sentence Extraction-Based Machine Reading Comprehension for Vietnamese](https://mlsanity.com/paper/2105.09043/) (2021)
- [Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks](https://mlsanity.com/paper/2101.10649/) (2021)
- [Accelerating Multilingual Language Model for Excessively Tokenized Languages](https://mlsanity.com/paper/2401.10660/) (2024)
- [VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2203.11400/) (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)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [LittleBird: Efficient Faster & Longer Transformer for Question Answering](https://mlsanity.com/paper/2210.11870/) (2022)
- [A Survey on Awesome Korean NLP Datasets](https://mlsanity.com/paper/2112.01624/) (2021)
- [Korean-Specific Dataset for Table Question Answering](https://mlsanity.com/paper/2201.06223/) (2022)
- [ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation](https://mlsanity.com/paper/2501.11929/) (2025)
- [K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts](https://mlsanity.com/paper/2606.02404/) (2026)
- [XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source](https://mlsanity.com/paper/2204.07002/) (2022)

## Abstract (arXiv)

Machine Reading Comprehension (MRC) is a task that requires machine to understand natural language and answer questions by reading a document. It is the core of automatic response technology such as chatbots and automatized customer supporting systems. We present Korean Question Answering Dataset(KorQuAD), a large-scale Korean dataset for extractive machine reading comprehension task. It consists of 70,000+ human generated question-answer pairs on Korean Wikipedia articles. We release KorQuAD1.0 and launch a challenge at https://KorQuAD.github.io to encourage the development of multilingual natural language processing research.