Natural question generation (QG) aims to generate questions from a passage
and an answer. Previous works on QG either (i) ignore the rich structure
information hidden in text, (ii) solely rely on cross-entropy loss that leads
to issues like exposure bias and inconsistency between train/test measurement,
or (iii) fail to fully exploit the answer information. To address these
limitations, in this paper, we propose a reinforcement learning (RL) based
graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq
generator with a novel Bidirectional Gated Graph Neural Network based encoder
to embed the passage, and a hybrid evaluator with a mixed objective combining
both cross-entropy and RL losses to ensure the generation of syntactically and
semantically valid text. We also introduce an effective Deep Alignment Network
for incorporating the answer information into the passage at both the word and
contextual levels. Our model is end-to-end trainable and achieves new
state-of-the-art scores, outperforming existing methods by a significant margin
on the standard SQuAD benchmark.

本篇文章提出了基于强化学习 (Graph2Seq) 模型的问题生成方法，该模型利用双向门控图神经网络作为 encoder 嵌入文章，并混合应用交叉熵损失和 RL 损失来确保生成语法和语义合理的文本。此外，文章还介绍了一种有效的深度对齐网络，将答案信息融合到文章中。该模型的端到端训练在标准 SQuAD 基准测试中取得了明显的最优成绩，超过了现有方法。