KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension

By Seungyoung Lim, Myungji Kim, Jooyoul Lee. 2019

103 citations recorded by the source corpus.

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 this https URL to encourage the development of multilingual natural language processing research.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, methodology): “XQuAD (Artetxe et al., 2019) includes 1,190 QA instances from SQuAD 1.1, with both questions and articles translated into 10 languages.”
MLQA: Evaluating Cross-lingual Extractive Question Answering (2019, background): “Lee et al. (2018) explore an approach to use English QA data from SQuAD to improve QA performance in Korean using an in-language seed dataset.”
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021, background): “KorQuAD 1.0 (Lim et al., 2019) is a Korean version of machine reading comprehension dataset.”
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2020, methodology): “KorQuAD 1.0 the Korean Question Answering Dataset (Lim et al., 2019), a Korean dataset also built following the SQuAD protocol.”
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2020, background): “0 for Korean (Lim et al., 2019), and MMQA for Hindi (Gupta et al., 2018).”
An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks (2020, methodology): “The KorQuAD 1.0 dataset (Lim et al., 2019) is a Korean adaptation of SQuAD 1.0 (Rajpurkar et al., 2016), a popular reading comprehension dataset.”
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model (2022, methodology): “(2021): NSMC3, KorQuAD (Lim et al., 2019), AI Hub translation4, and YNAT (Park et al.”
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models (2020, methodology): “0 dataset (Lim et al., 2019), which was crowdsourced from Korean Wikipedia article and introduced as a public benchmark in the same way as SQuAD.”
A Comprehensive Overview of Large Language Models (2023, unknown): “0 [315], CAIL2018-Task1 & Task2 [316] Mathematical Reasoning MATH [317], Math23k [318], GSM8K [319], MathQA [320], MGSM [321], MultiArith [322], ASDiv [323], MAWPS [324], SVAMP [325] Problem Solving HumanEval [326], DS-1000 [327], MBPP [328], APPS [317], CodeContests [120] Natural Language Inference”
Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021, background): “rQuAD (Lim et al., 2019), researchers have proposed MRC models even surpassing human scores.”