generated multiple questions via rule-based tree transformations of the constituency parse of a declarative sentence and then ranked them using a logistic-regression ranker with manually designed features. @yao2010question described transformations of Minimal Recursion Semantics representations guaranteeing grammaticality. Other transformations have been in the past defined in terms of templates [@mazidi2014linguistic; @mazidi2015leveraging; @mazidi2016infusing; @flor2018semantic], or explicitly performed [@heilmanquestion] by searching tree patterns via Tregex, followed by their manipulation using Tsurgeon [@levy2006tregex]. @kurdi2020systematic provide a comprehensive summary of QG, analysing and comparing approaches before and after 2014.

Vis-à-vis current neural question generators, rule-based architectures are highly transparent, easily extensible, and generate well-formed questions since they perform clearly defined syntactic transformations like subject-auxiliary inversion and *WH-movement* over parse structures whilst leveraging fundamental NLP annotations like named entities, co-reference, temporal entities, etc.

However, most of the existing rule-based systems have lacked diversity, being mostly focused on generating *What*-type and boolean questions and have mainly exploited parse structures which are not semantically informed. @mazidi2016infusing [@flor2018semantic] use Dependency, SRL, and NER templates but do not handle modalities and negation in a robust manner. Moreover, there is plenty of availability of core linguistic resources like VerbNet and PropBank, which provide further unique ways to look at sentences 