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JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension

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arxiv 2202.01764 v1 pith:JA2TTFLC submitted 2022-02-03 cs.CL cs.AIcs.LG

ByungHoon So , Kyuhong Byun , Kyungwon Kang , Seongjin Cho This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords jaquadquestionansweringdatasetjapaneseannotatedmachineachieves
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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.

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  1. A Collection of Question Answering Datasets for Norwegian

    cs.CL 2025-01 conditional novelty 6.0 of 10

    Four Norwegian QA datasets covering both written standards are released, and 11 language models are benchmarked on them.

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