  Recent years have seen a dramatic expansion of tasks and datasets posed as
question answering, from reading comprehension, semantic role labeling, and
even machine translation, to image and video understanding. With this
expansion, there are many differing views on the utility and definition of
"question answering" itself. Some argue that its scope should be narrow, or
broad, or that it is overused in datasets today. In this opinion piece, we
argue that question answering should be considered a format which is sometimes
useful for studying particular phenomena, not a phenomenon or task in itself.
We discuss when a task is correctly described as question answering, and when a
task is usefully posed as question answering, instead of using some other
format.
