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The state of the art in open-domain question answering (QA) relies on anefficient retriever that drastically reduces the search space for the expensivereader. A rather overlooked question in the community is the relationshipbetween the retriever and the reader, and in particular, if the whole purposeof the retriever is just a fast approximation for the reader. Our empiricalevidence indicates that the answer is no, and that the reader and the retrieverare complementary to each other even in terms of accuracy only. We make acareful conjecture that the architectural constraint of the retriever, whichhas been originally intended for enabling approximate search, seems to alsomake the model more robust in large-scale search. We then propose to distillthe reader into the retriever so that the retriever absorbs the strength of thereader while keeping its own benefit. Experimental results show that our methodcan enhance the document recall rate as well as the end-to-end QA accuracy ofoff-the-shelf retrievers in open-domain QA tasks.

Is Retriever Merely an Approximator of Reader?