with the event “paid” results in a question with a stateful event of “cost”. Deducible questions like these have a good chance of having a distribution of n-grams quite different from the source sentences, possibly exposing the weakness of traditional n-gram metrics and rendering them less useful for a task like QG.

In order to have a complete and more reliable evaluation to gauge the system, we also carry out a human evaluation using two of the metrics used in QG-STEC Task B [@rus2012detailed], namely grammaticality, and relevance which we define below. We compared the questions generated from our system against the constituency-based H&S [@heilmanquestion], a neural system NQG [@du2017learning] which does not depend on a separate answer extractor and QPP&QAP[^5] [@zhang2019addressing] which has outperformed existing methods. We fed a total of 100 facts randomly picked from Wikipedia and 5 commercial domains (IT, Healthcare, Sports, Banking and Politics) combined, to each of the four systems. We then conducted a crowd-sourced evaluation over Amazon Mechanical Turk for the generated questions.

-   **Grammatical Correctness**: Raters had to rate a question on how grammatically correct it is or how syntactically fluent it is, disregarding its underlying meaning.

-   **Relevance Score**: Raters had to 