ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages
<|reference_start|>ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages: Question answering (QA) and Machine Reading Comprehension (MRC) tasks have significantly advanced in recent years due to the rapid development of deep learning techniques and, more recently, large language models. At the same time, many benchmark datasets have become available for QA and MRC tasks. However, most existing large-scale benchmark datasets have been created predominantly using synchronous document collections like Wikipedia or the Web. Archival document collections, such as historical newspapers, contain valuable information from the past that is still not widely used to train large language models. To further contribute to advancing QA and MRC tasks and to overcome the limitation of previous datasets, we introduce ChroniclingAmericaQA, a large-scale temporal QA dataset with 487K question-answer pairs created based on the historical newspaper collection Chronicling America. Our dataset is constructed from a subset of the Chronicling America newspaper collection spanning 120 years. One of the significant challenges for utilizing digitized historical newspaper collections is the low quality of OCR text. Therefore, to enable realistic testing of QA models, our dataset can be used in three different ways: answering questions from raw and noisy content, answering questions from cleaner, corrected version of the content, as well as answering questions from scanned images of newspaper pages. This and the fact that ChroniclingAmericaQA spans the longest time period among available QA datasets make it quite a unique and useful resource.<|reference_end|>
@article{piryani2024chroniclingamericaqa:,
    title={ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on
  Historical American Newspaper Pages},
    author={Bhawna Piryani, Jamshid Mozafari, Adam Jatowt},
    journal={Proceedings of the 47th International ACM SIGIR Conference on
  Research and Development in Information Retrieval (SIGIR 2024)},
    year={2024},
    doi={10.1145/3626772.3657891},
    archivePrefix={arXiv},
    eprint={2403.17859},
    primaryClass={cs.CL}
}