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Neural models for question answering (QA) over documents have achievedsignificant performance improvements. Although effective, these models do notscale to large corpora due to their complex modeling of interactions betweenthe document and the question. Moreover, recent work has shown that such modelsare sensitive to adversarial inputs. In this paper, we study the minimalcontext required to answer the question, and find that most questions inexisting datasets can be answered with a small set of sentences. Inspired bythis observation, we propose a simple sentence selector to select the minimalset of sentences to feed into the QA model. Our overall system achievessignificant reductions in training (up to 15 times) and inference times (up to13 times), with accuracy comparable to or better than the state-of-the-art onSQuAD, NewsQA, TriviaQA and SQuAD-Open. Furthermore, our experimental resultsand analyses show that our approach is more robust to adversarial inputs.

Efficient and Robust Question Answering from Minimal Context over Documents