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.

本文提出了 RE$^3$QA，一种统一的问答模型，它结合了上下文检索、阅读理解和答案重排序来预测最终答案。与以往的流水线方法不同，RE$^3$QA 跨不同的组件共享上下文化的文本表示，并设计了高质量的上游输出来直接监督下游模块，从而避免了上下文不一致的问题，实现了端到端的训练。实验证明，我们的模型优于管道基线，在两个版本的 TriviaQA 和两个变体的 SQuAD 上实现了最先进的结果。