question-answer pairs from a declarative sentence. It has direct use in education and generating engagement, where a system automatically generates questions about passages that someone has read. A more recent secondary use is for automatic generation of questions as a data augmentation approach for training Question Answering (QA) systems. QG was initially approached by syntactic rules for question-generation, followed by some form of statistical ranking of goodness, e.g.,  [@heilmanquestion; @heilman2010good]. In recent years, as in most areas of NLP, the dominant approach has been neural network generation [@du2017learning], in particular using a sequence-to-sequence architecture, which exploits the data in the rapidly growing number of large QA data sets.

![ The SRL structure is leveraged to invoke a template, and a simple rearrangement of the modifying arguments is performed.[]{data-label="fig:srl_qgen"}](QG_asT3.png){width="\linewidth"}

Previous rule-based approaches suffer from a significant lack of variety in the questions they generate, sticking to a few simple and reliable syntactic transformation patterns. Neural architectures provide a pathway to solving this limitation since they can exploit QA datasets to learn the broad array of human question types, providing the usual neural network advantages of a data-exploiting, end-to-end trainable architecture. Nevertheless, we observe that the quality of current neural QG systems 