Question Answering Resources Applied to Slot-Filling

We investigate the utility of pre-existing question answering models and data for a recently proposed relation extraction task. We find that in the low-resource and zero-shot cases, such resources are surprisingly useful. Moreover, the resulting models show robust performance on a new test set we create from the task's original datasets.