  Information Extraction (IE) researchers are mapping tasks to Question
Answering (QA) in order to leverage existing large QA resources, and thereby
improve data efficiency. Especially in template extraction (TE), mapping an
ontology to a set of questions can be more time-efficient than collecting
labeled examples. We ask whether end users of TE systems can design these
questions, and whether it is beneficial to involve an NLP practitioner in the
process. We compare questions to other ways of phrasing natural language
prompts for TE. We propose a novel model to perform TE with prompts, and find
it benefits from questions over other styles of prompts, and that they do not
require an NLP background to author.
