mlsanityData to 7 Oct 2026

JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension

arXiv 2202.01764 ↗First posted 3 Feb 2022cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

Question Answering (QA) is a task in which a machine understands a given document and a question to find an answer. Despite impressive progress in the NLP area, QA is still a challenging problem, especially for non-English languages due to the lack of annotated datasets. In this paper, we present the Japanese Question Answering Dataset, JaQuAD, which is annotated by humans. JaQuAD consists of 39,696 extractive question-answer pairs on Japanese Wikipedia articles. We finetuned a baseline model which achieves 78.92% for F1 score and 63.38% for EM on test set. The dataset and our experiments are available at https://github.com/SkelterLabsInc/JaQuAD.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

14 of 26 references.

References · 14

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  5. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  6. 2018CoQA: A Conversational Question Answering Challengecited by 674
  7. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 575
  8. 2018QuAC : Question Answering in Contextcited by 379
  9. 2019MLQA: Evaluating Cross-lingual Extractive Question Answeringcited by 339
  10. 2021KLUE: Korean Language Understanding Evaluationcited by 113
  11. 2020FQuAD: French Question Answering Datasetcited by 58
  12. 2021GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrievalcited by 39
2 more
  1. 2019SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysiscited by 38
  2. 2017Stochastic Answer Networks for Machine Reading Comprehensioncited by 22

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Cited by year

Citing papers per year: 2022: 3; 2023: 4.

7 papers.

  1. 2022Language Modelling with Pixelscited by 53
  2. 2022Generative Language Models for Paragraph-Level Question Generationcited by 28
  3. 2023An Efficient Multilingual Language Model Compression through Vocabulary Trimmingcited by 19
  4. 2023A Practical Toolkit for Multilingual Question and Answer Generationcited by 10
  5. 2023NorQuAD: Norwegian Question Answering Datasetcited by 7
  6. 2022XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Sourcecited by 2
  7. 2023Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answeringcited by 0