Abstract
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 RE3QA, a unified question
answering model that combines context retrieving, reading comprehension, and answer
reranking to predict the final answer. Unlike
previous pipelined approaches, RE3QA 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-ofthe-art results on two versions of TriviaQA and
two variants of SQuAD.