In our first experiment, we examine the utility of a standard QA dataset as training data for the slotfilling model of <cite>Levy et al. (2017)</cite> . 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) both positive examples, containing answers, and negative examples, without answers.