Question generation over knowledge bases (KBQG) aims at generating
natural-language questions about a subgraph, i.e. a set of (connected) triples.
Two main challenges still face the current crop of encoder-decoder-based
methods, especially on small subgraphs: (1) low diversity and poor fluency due
to the limited information contained in the subgraphs, and (2) semantic drift
due to the decoder's oblivion of the semantics of the answer entity. We propose
an innovative knowledge-enriched, type-constrained and grammar-guided KBQG
model, named KTG, to addresses the above challenges. In our model, the encoder
is equipped with auxiliary information from the KB, and the decoder is
constrained with word types during QG. Specifically, entity domain and
description, as well as relation hierarchy information are considered to
construct question contexts, while a conditional copy mechanism is incorporated
to modulate question semantics according to current word types. Besides, a
novel reward function featuring grammatical similarity is designed to improve
both generative richness and syntactic correctness via reinforcement learning.
Extensive experiments show that our proposed model outperforms existing methods
by a significant margin on two widely-used benchmark datasets SimpleQuestion
and PathQuestion.

本研究旨在解决在知识图谱子图上生成自然语言问题时，信息量不足和语义漂移的问题，提出了一种名为 KTG 的知识增强的、类型约束和语法引导的 KBQG 模型，并设计了一种奖励函数来提高生成的丰富性和句法正确性，实验证明该模型在两个广泛使用的基准数据集 SimpleQuestion 和 PathQuestion 上优于现有方法。