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

Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension