back-translation to 1) get rid of grammatical errors propagated from incorrect parsing and template restrictions, and 2) eliminate rarely used Prop-Bank descriptions and generate highly probable questions.

While previous work in rule-based QG has used SRL templates and WordNet senses to describe the roles arguments around a verb play, previous SRL templates have always been verb-agnostic, and we believe there is a great deal of potential in PropBank descriptions. Moreover, WordNet supersenses do not always give rise to acceptable questions. On manual evaluation, question relevance decreased after incorporating templates with WordNet supersenses. Instead, we make use of PropBank’s verb-specific natural language argument descriptions to create an additional set of templates. VerbNet senses have a one-to-one mapping with PropBank rolesets via the SemLink project [@palmer2009semlink]. We hence make use of  @brown2019verbnet’s parser to find the appropriate PropBank roleset for a sentence.

However, we observed that a lot of PropBank descriptions were noisy and made use of phrases which would be unarguably rare in ordinary parlance like “breather” or “truster”. To eliminate such descriptions, we computed the mean Google N-gram probabilities [@lin2012syntactic] of all the PropBank phrases in the timespan of the last 100 years and kept only those phrases which ranked 