TITLE: JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension 

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.
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.
JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
http://arxiv.org/abs/2202.01764v1