  In the last few years, open-domain question answering (ODQA) has advanced
rapidly due to the development of deep learning techniques and the availability
of large-scale QA datasets. However, the current datasets are essentially
designed for synchronic document collections (e.g., Wikipedia). Temporal news
collections such as long-term news archives spanning several decades, are
rarely used in training the models despite they are quite valuable for our
society. To foster the research in the field of ODQA on such historical
collections, we present ArchivalQA, a large question answering dataset
consisting of 532,444 question-answer pairs which is designed for temporal news
QA. We divide our dataset into four subparts based on the question difficulty
levels and the containment of temporal expressions, which we believe are useful
for training and testing ODQA systems characterized by different strengths and
abilities. The novel QA dataset-constructing framework that we introduce can be
also applied to generate non-ambiguous questions of good quality over other
types of temporal document collections.
