# JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension

Source: [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension](<https://www.sota2.com/research/paper/68fa6bb61e209131cea2fc54>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T14:08:58.121Z

Authors: ByungHoon So, Kyuhong Byun, Kyungwon Kang, Seongjin Cho

Published: 2022-02-03T18:40:25.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2202.01764>)

[PDF](<https://arxiv.org/pdf/2202.01764.pdf>)

[Implementation](<https://github.com/SkelterLabsInc/JaQuAD>)