  The state of the art in open-domain question answering (QA) relies on an
efficient retriever that drastically reduces the search space for the expensive
reader. A rather overlooked question in the community is the relationship
between the retriever and the reader, and in particular, if the whole purpose
of the retriever is just a fast approximation for the reader. Our empirical
evidence indicates that the answer is no, and that the reader and the retriever
are complementary to each other even in terms of accuracy only. We make a
careful conjecture that the architectural constraint of the retriever, which
has been originally intended for enabling approximate search, seems to also
make the model more robust in large-scale search. We then propose to distill
the reader into the retriever so that the retriever absorbs the strength of the
reader while keeping its own benefit. Experimental results show that our method
can enhance the document recall rate as well as the end-to-end QA accuracy of
off-the-shelf retrievers in open-domain QA tasks.
