This paper explores the task of answer-aware questions generation. Based on
the attention-based pointer generator model, we propose to incorporate an
auxiliary task of language modeling to help question generation in a
hierarchical multi-task learning structure. Our joint-learning model enables
the encoder to learn a better representation of the input sequence, which will
guide the decoder to generate more coherent and fluent questions. On both SQuAD
and MARCO datasets, our multi-task learning model boosts the performance,
achieving state-of-the-art results. Moreover, human evaluation further proves
the high quality of our generated questions.

本文探讨了答案感知的问题生成任务，提出了在基于注意力机制的指针生成模型中，通过将语言建模转化为辅助任务，将问题生成纳入分层多任务学习结构。我们的联合学习模型使编码器能够学习到更好的输入序列表示，从而指导解码器生成更连贯、流畅的问题。在 SQuAD 和 MARCO 数据集上，我们的多任务学习模型提高了性能，取得了最先进的结果。此外，人类评估进一步证明了我们生成的问题的高质量。