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A recent state-of-the-art neural open information extraction (OpenIE) systemgenerates extractions iteratively, requiring repeated encoding of partialoutputs. This comes at a significant computational cost. On the other hand,sequence labeling approaches for OpenIE are much faster, but worse inextraction quality. In this paper, we bridge this trade-off by presenting aniterative labeling-based system that establishes a new state of the art forOpenIE, while extracting 10x faster. This is achieved through a novel IterativeGrid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labelingtask. We improve its performance further by applying coverage (soft)constraints on the grid at training time.  Moreover, on observing that the best OpenIE systems falter at handlingcoordination structures, our OpenIE system also incorporates a new coordinationanalyzer built with the same IGL architecture. This IGL based coordinationanalyzer helps our OpenIE system handle complicated coordination structures,while also establishing a new state of the art on the task of coordinationanalysis, with a 12.3 pts improvement in F1 over previous analyzers. Our OpenIEsystem, OpenIE6, beats the previous systems by as much as 4 pts in F1, whilebeing much faster.

OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction