  We study open-domain question answering (ODQA) with structured, unstructured
and semi-structured knowledge sources, including text, tables, lists, and
knowledge bases. Our approach homogenizes all sources by reducing them to text,
and applies recent, powerful retriever-reader models which have so far been
limited to text sources only. We show that knowledge-base QA can be greatly
improved when reformulated in this way. Contrary to previous work, we find that
combining sources always helps, even for datasets which target a single source
by construction. As a result, our unified model produces state-of-the-art
results on 3 popular ODQA benchmarks.
