Neural models for question answering (QA) over documents have achieved
significant performance improvements. Although effective, these models do not
scale to large corpora due to their complex modeling of interactions between
the document and the question. Moreover, recent work has shown that such models
are sensitive to adversarial inputs. In this paper, we study the minimal
context required to answer the question, and find that most questions in
existing datasets can be answered with a small set of sentences. Inspired by
this observation, we propose a simple sentence selector to select the minimal
set of sentences to feed into the QA model. Our overall system achieves
significant reductions in training (up to 15 times) and inference times (up to
13 times), with accuracy comparable to or better than the state-of-the-art on
SQuAD, NewsQA, TriviaQA and SQuAD-Open. Furthermore, our experimental results
and analyses show that our approach is more robust to adversarial inputs.

该研究探讨了答题所需的最小上下文，并提出了一种简单的句子选择器用于选择最小的句子来喂养问答模型，以实现训练和推理时间的显著减少并提高精度，并且更加鲁棒。