  Open Information Extraction (OpenIE) has been used in the pipelines of
various NLP tasks. Unfortunately, there is no clear consensus on which models
to use in which tasks. Muddying things further is the lack of comparisons that
take differing training sets into account. In this paper, we present an
application-focused empirical survey of neural OpenIE models, training sets,
and benchmarks in an effort to help users choose the most suitable OpenIE
systems for their applications. We find that the different assumptions made by
different models and datasets have a statistically significant effect on
performance, making it important to choose the most appropriate model for one's
applications. We demonstrate the applicability of our recommendations on a
downstream Complex QA application.
