Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers. In addition, most of previous works built on either RNN-based or Transformer based models to encode a linearized KG sugraph, which totally discards the explicit structure information of a KG subgraph. To address this issue, we propose to apply a bidirectional Graph2Seq model to encode the KG subgraph. Furthermore, we enhance our RNN decoder with node-level copying mechanism to allow directly copying node attributes from the KG subgraph to the output question. Both automatic and human evaluation results demonstrate that our model achieves new state-of-the-art scores, outperforming existing methods by a significant margin on two QG benchmarks. Experimental results also show that our QG model can consistently benefit the Question Answering (QA) task as a mean of data augmentation.

翻译：知识图谱问题生成旨在从知识图谱和目标答案中生成自然语言问题。以往工作大多聚焦于从单个知识图谱三元组生成问题的简单设定。本文着眼于更实际的场景，即从知识图谱子图和目标答案中生成问题。此外，以往工作多基于RNN或Transformer模型对线性化的知识图谱子图进行编码，完全忽略了知识图谱子图的显式结构信息。为解决此问题，我们提出采用双向Graph2Seq模型对知识图谱子图进行编码。同时，我们在RNN解码器中引入节点级复制机制，允许直接从知识图谱子图中复制节点属性至输出问题。自动评估与人工评估结果表明，我们的模型在两个问题生成基准测试中取得了新的最佳成绩，显著超越现有方法。实验还显示，模型生成的问题可作为数据增强手段，持续提升问答任务性能。