We propose a two-stage neural model to tackle question generation from
documents. First, our model estimates the probability that word sequences in a
document are ones that a human would pick when selecting candidate answers by
training a neural key-phrase extractor on the answers in a question-answering
corpus. Predicted key phrases then act as target answers and condition a
sequence-to-sequence question-generation model with a copy mechanism.
Empirically, our key-phrase extraction model significantly outperforms an
entity-tagging baseline and existing rule-based approaches. We further
demonstrate that our question generation system formulates fluent, answerable
questions from key phrases. This two-stage system could be used to augment or
generate reading comprehension datasets, which may be leveraged to improve
machine reading systems or in educational settings.

本文提出了一个两阶段的神经模型来解决从文档中生成问题的问题，第一阶段使用神经关键短语提取器来预测关键短语并作为目标答案，第二阶段使用序列到序列的问题生成模型带有复制机制生成问题，该模型优于标签基线和规则化的方法，是提高机器阅读系统或教育工作者的良好选择