We present a novel neural architecture for answering queries, designed to
optimally leverage explicit support in the form of query-answer memories. Our
model is able to refine and update a given query while separately accumulating
evidence for predicting the answer. Its architecture reflects this separation
with dedicated embedding matrices and loosely connected information pathways
(modules) for updating the query and accumulating evidence. This separation of
responsibilities effectively decouples the search for query related support and
the prediction of the answer. On recent benchmark datasets for reading
comprehension, our model achieves state-of-the-art results. A qualitative
analysis reveals that the model effectively accumulates weighted evidence from
the query and over multiple support retrieval cycles which results in a robust
answer prediction.

本文提出了一种新的神经体系结构，旨在充分利用查询 - 答案记忆体的显式支持，以回答问题。我们的模型能够在单独累积预测答案证据的同时，对给定的查询进行细化和更新。通过该体系结构的松散连接信息通路（模块），我们的模型有效地分离了与查询相关的支持搜索和答案预测。根据阅读理解的最新基准数据集，我们的模型取得了最先进的结果。定性分析表明，模型能够有效地累积来自查询和多次支持检索的加权证据，从而实现了强大的答案预测。