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We investigate applying repurposed generic QA data and models to a recentlyproposed relation extraction task. We find that training on SQuAD producesbetter zero-shot performance and more robust generalisation compared to thetask 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 needfor model modification.

Reduce, Reuse, Recycle: New uses for old QA resources