<cite>Levy et al. (2017)</cite> present a reformulation of RE, where the task is framed as reading comprehension. In this formulation, each relation type (e.g. author, occupation) is mapped to at least one natural language question template (e.g. "Who is the author of x?"), where x is filled with an entity (e.g. "Inferno"). The model is then tasked with finding an answer ("Dante Alighieri") to this question with respect to a given context. They show that this formulation of the problem both outperforms off-the-shelf RE systems in the typical RE setting and, in addition, enables generalization to unspecified and unseen types of relations.