Wikipedia measuring the grammaticality and relevance before and after back-translation. While grammaticality increased from 3.54 to 4.11, question relevance fell a bit from 3.96 to 3.88. This observation, along with the performance of QPP&QAP shown in Table \[results-table\], accentuates that while neural models are learning syntactic structures well, there is still some progress to be made to generate relevant questions.

Discussion
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We introduced Syn-QG, a set of broad coverage rules leveraging event-based and sub-event based sentence views along with verb-specific argument descriptions. Automatic and manual evaluations show that Syn-QG is able to generate a large number of diverse and highly relevant questions with better fluency. Verb-focused rules help approach long-distance dependencies and reduce the need for explicit sentence simplification by breaking down a sentence into clauses while custom rules like implications serve a purpose similar to a re-ranker to discard irrelevant questions but with increased determinism. While our work focuses on sentence-level QG, it would be interesting to see how questions generated from VerbNet predicates would have an impact on multi-sentence or passage level QG, where the verb-agnostic states of the participants would change as a function of multiple verbs. The larger goal of QG is currently far from 