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KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension

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arxiv 1909.07005 v2 pith:MCJMLIPQ submitted 2019-09-16 cs.CL

Seungyoung Lim , Myungji Kim , Jooyoul Lee This is my paper
classification cs.CL
keywords koreanmachinereadingcomprehensiondatasetkorquadkorquad1language
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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  1. FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation

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    A hierarchical Q-Former compresses speech to about 1.67 tokens/sec, enabling hour-long audio processing with near-linear memory scaling and competitive benchmark scores.

  2. Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A new Korean benchmark, KoSEnd, shows LLMs have limited grasp of Korean sentence endings, and warning them about potentially missing endings improves their choices.

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