  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.
