Open RE methods (Yates et al., 2007; Fader et al., 2011) do not require relationspecific data, but treat different phrasings of the same relation as different relations and rely on a combination of syntactic features (e.g. dependency parses) and normalisation rules, and so have limited generalization capacity. Zero-shot relation extraction <cite>Levy et al. (2017)</cite> propose a novel approach towards achieving this generalization by transforming relations into natural language question templates. For instance, the relation born in(x, y) can be expressed as "Where was x born?" or "In which place was x born?". Then, a reading comprehension model (Seo et al., 2016; Chen et al., 2017) can be trained on question, answer, and context examples where the x slot is filled with an entity and the y slot is either an answer if the answer is present in the context, or NIL. The model is then able to extract relation instances (given expressions of the relations as questions) from raw text.