superior but are sometimes slightly irrelevant as compared to their original counterparts.

Related Work
============

With the advent of large-scale QA datasets [@rajpurkar2016squad; @nguyen2016ms], recent work in QG [@du2017learning; @zhou2017neural] has primarily focused on training sequence-to-sequence and attention-based architectures. @dong2019unified fine-tuned the question generation task by taking advantage of a large pre-trained language model. Success in reinforcement learning has inspired teacher-student frameworks [@wang2017joint; @tang2017question] treating QA and QG as complementary tasks and performing joint training by using results from QA as rewards for the QG task. @yuan-etal-2017-machine [@Hosking_2019; @zhang2019addressing] used evaluation metrics like BLEU, sentence perplexity, and QA probability as rewards for dealing with exposure bias.

 @chen2019natural trained a reinforcement learning based graph-to-sequence architecture by embedding the passage via a novel gated bi-directional graph neural network and generating the question via a recurrent neural network. To estimate the positions of copied words, @liu2019learning used a graph convolution network and convolved over the nodes of the dependency parse of the passage. @li2019improving jointly modeled OpenIE relations along with the passage using a gated-attention mechanism and a dual copy mechanism.

Traditionally, question generation has been tackled by numerous rule-based approaches [@heilmanquestion; @mostow2009generating; @yao2010question; @lindberg2013generating; @labutov2015deep]. @heilmanquestion [@heilman2010good] introduced an overgenerate-and-rank approach that 