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 dataset with 485K 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.

基于历史报纸收藏物 Chronicling America 构建的大规模数据集 ChroniclingAmericaQA 是用于问题回答和机器阅读理解任务的独特而有用的资源。