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 performsRetrieval asAttention (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 athttps://github.com/jzbjyb/ReAtt.