  We present 1-Pager the first system that answers a question and retrieves
evidence using a single Transformer-based model and decoding process. 1-Pager
incrementally partitions the retrieval corpus using constrained decoding to
select a document and answer string, and we show that this is competitive with
comparable retrieve-and-read alternatives according to both retrieval and
answer accuracy metrics. 1-Pager also outperforms the equivalent closed-book
question answering model, by grounding predictions in an evidence corpus. While
1-Pager is not yet on-par with more expensive systems that read many more
documents before generating an answer, we argue that it provides an important
step toward attributed generation by folding retrieval into the
sequence-to-sequence paradigm that is currently dominant in NLP. We also show
that the search paths used to partition the corpus are easy to read and
understand, paving a way forward for interpretable neural retrieval.
