  We investigate applying repurposed generic QA data and models to a recently
proposed relation extraction task. We find that training on SQuAD produces
better zero-shot performance and more robust generalisation compared to the
task specific training set. We also show that standard QA architectures (e.g.
FastQA or BiDAF) can be applied to the slot filling queries without the need
for model modification.
