Existing question answering datasets focus on
dealing with homogeneous information, based
either only on text or KB/Table information
alone. However, as human knowledge is
distributed over heterogeneous forms, using
homogeneous information might lead to severe coverage problems. To fill in the gap,
we present HYBRIDQA, a new large-scale
question-answering dataset that requires reasoning on heterogeneous information. Each
question is aligned with a structured Wikipedia
table and multiple free-form corpora linked
with the entities in the table. The questions are
designed to aggregate both tabular information
and text information, i.e. lack of either form
would render the question unanswerable.
