Neural question generation (NQG) is the task of generating a question from a
given passage with deep neural networks. Previous NQG models suffer from a
problem that a significant proportion of the generated questions include words
in the question target, resulting in the generation of unintended questions. In
this paper, we propose answer-separated seq2seq, which better utilizes the
information from both the passage and the target answer. By replacing the
target answer in the original passage with a special token, our model learns to
identify which interrogative word should be used. We also propose a new module
termed keyword-net, which helps the model better capture the key information in
the target answer and generate an appropriate question. Experimental results
demonstrate that our answer separation method significantly reduces the number
of improper questions which include answers. Consequently, our model
significantly outperforms previous state-of-the-art NQG models.

本文介绍了一种基于深度神经网络的答案分离序列到序列模型，该模型通过用特殊令牌替换原始段落中的目标答案来预测出应该使用哪个疑问词，并使用关键词网络模块来更好地捕捉目标答案中的关键信息，从而显著减少了包含答案的不恰当问题的数量，因此超越了之前最先进的神经问题生成模型。