  This paper considers the reading comprehension task in which multiple
documents are given as input. Prior work has shown that a pipeline of
retriever, reader, and reranker can improve the overall performance. However,
the pipeline system is inefficient since the input is re-encoded within each
module, and is unable to leverage upstream components to help downstream
training. In this work, we present RE$^3$QA, a unified question answering model
that combines context retrieving, reading comprehension, and answer reranking
to predict the final answer. Unlike previous pipelined approaches, RE$^3$QA
shares contextualized text representation across different components, and is
carefully designed to use high-quality upstream outputs (e.g., retrieved
context or candidate answers) for directly supervising downstream modules
(e.g., the reader or the reranker). As a result, the whole network can be
trained end-to-end to avoid the context inconsistency problem. Experiments show
that our model outperforms the pipelined baseline and achieves state-of-the-art
results on two versions of TriviaQA and two variants of SQuAD.
