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Intrinsic evaluations of OIE systems are carried out either manually -- withhuman evaluators judging the correctness of extractions -- or automatically, onstandardized benchmarks. The latter, while much more cost-effective, is lessreliable, primarily because of the incompleteness of the existing OIEbenchmarks: the ground truth extractions do not include all acceptable variantsof the same fact, leading to unreliable assessment of the models' performance.Moreover, the existing OIE benchmarks are available for English only. In thiswork, we introduce BenchIE: a benchmark and evaluation framework forcomprehensive evaluation of OIE systems for English, Chinese, and German. Incontrast to existing OIE benchmarks, BenchIE is fact-based, i.e., it takes intoaccount informational equivalence of extractions: our gold standard consists offact synsets, clusters in which we exhaustively list all acceptable surfaceforms of the same fact. Moreover, having in mind common downstream applicationsfor OIE, we make BenchIE multi-faceted; i.e., we create benchmark variants thatfocus on different facets of OIE evaluation, e.g., compactness or minimality ofextractions. We benchmark several state-of-the-art OIE systems using BenchIEand demonstrate that these systems are significantly less effective thanindicated by existing OIE benchmarks. We make BenchIE (data and evaluationcode) publicly available on https://github.com/gkiril/benchie.

BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation