is still lacking: The generated questions lack syntactic fluency, and the models lack transparency and an easy way to improve them.

We argue that in essence QG can be governed by simple syntactic “question transformations” – while the implementation details vary, this is in accord with all major linguistic viewpoints, such as Construction Grammar and Chomskyan Generative Grammar, which emphasize grammatical rules and the existence of finite ways to create novel utterances. However, successful, fluent question generation requires more than just understanding syntactic question transformations, since felicitous questions must also observe various semantic and pragmatic constraints. We approach these by making use of semantic role labelers (SRL), previously unexploited linguistic semantic resources like VerbNet’s predicates (Figure \[fig:vn4\]) and PropBank’s rolesets and custom rules like implications, allowing us to generate a broader range of questions of a descriptive and inferential nature. A simple transformation commonly used in rule-based QG is also displayed in Figure \[fig:srl\_qgen\].

We evaluate our QG framework, Syn-QG against three QG systems on a mixture of Wikipedia and commercial text sentences outperforming existing approaches in grammaticality and relevance in a crowd-sourced human evaluation while simultaneously generating more types of questions. We also notice that back-translated questions are grammatically 