  Systems for knowledge-intensive tasks such as open-domain question answering
(QA) usually consist of two stages: efficient retrieval of relevant documents
from a large corpus and detailed reading of the selected documents to generate
answers. Retrievers and readers are usually modeled separately, which
necessitates a cumbersome implementation and is hard to train and adapt in an
end-to-end fashion. In this paper, we revisit this design and eschew the
separate architecture and training in favor of a single Transformer that
performs Retrieval as Attention (ReAtt), and end-to-end training solely based
on supervision from the end QA task. We demonstrate for the first time that a
single model trained end-to-end can achieve both competitive retrieval and QA
performance, matching or slightly outperforming state-of-the-art separately
trained retrievers and readers. Moreover, end-to-end adaptation significantly
boosts its performance on out-of-domain datasets in both supervised and
unsupervised settings, making our model a simple and adaptable solution for
knowledge-intensive tasks. Code and models are available at
https://github.com/jzbjyb/ReAtt.
