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

Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases