JDocQA: Japanese Document Question Answering Dataset for Generative Language Models

Eri Onami, Shuhei Kurita, Taiki Miyanishi, Taro Watanabe

Abstract
Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites, and it is a truly demanding task as paper and electronic forms of documents are so common in our society. This is known as a quite challenging task because it requires not only text understanding but also understanding of figures and tables, and hence visual question answering (VQA) methods are often examined in addition to textual approaches. We introduce Japanese Document Question Answering (JDocQA), a large-scale document-based QA dataset, essentially requiring both visual and textual information to answer questions, which comprises 5,504 documents in PDF format and annotated 11,600 question-and-answer instances in Japanese. Each QA instance includes references to the document pages and bounding boxes for the answer clues. We incorporate multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. We empirically evaluate the effectiveness of our dataset with text-based large language models (LLMs) and multimodal models. Incorporating unanswerable questions in finetuning may contribute to harnessing the so-called hallucination generation.
Anthology ID:
2024.lrec-main.830
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
9503–9514
Language:
URL:
https://aclanthology.org/2024.lrec-main.830
DOI:
Bibkey:
Cite (ACL):
Eri Onami, Shuhei Kurita, Taiki Miyanishi, and Taro Watanabe. 2024. JDocQA: Japanese Document Question Answering Dataset for Generative Language Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9503–9514, Torino, Italia. ELRA and ICCL.
Cite (Informal):
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models (Onami et al., LREC-COLING 2024)
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