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. In order to foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 1,067,056 question-answer pairs which is designed for temporal news QA. In addition, we create four subparts of our dataset based on the question difficulty levels and the containment of temporal expressions, which we believe could be useful for training or testing ODQA systems characterized by different strengths and abilities. The novel QA dataset-constructing framework that we introduce can be also applied to create datasets over other types of collections.
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