In our first experiment, we examine the utility of a QA dataset in relation to the slot-filling task of <cite>Levy et al. (2017)</cite> . <cite>Their</cite> zero-shot model generalised from seen relations to unseen relations by translating all relations into natural language question templates, such as Where was XXX born? for the relation place of birth. Identifying an instance of such a relation in text is then equivalent to finding an answer to the relevant question template, instantiated with the appropriate entity. However, such a model also needs to be able to identify when no answer is found in the text, and to achieve this <cite>they</cite> trained a slightly modified version of BiDAF (Seo et al., 2016) (Rajpurkar et al., 2016) , can be applied to the relation extraction task.