  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.
