  A recent state-of-the-art neural open information extraction (OpenIE) system
generates extractions iteratively, requiring repeated encoding of partial
outputs. This comes at a significant computational cost. On the other hand,
sequence labeling approaches for OpenIE are much faster, but worse in
extraction quality. In this paper, we bridge this trade-off by presenting an
iterative labeling-based system that establishes a new state of the art for
OpenIE, while extracting 10x faster. This is achieved through a novel Iterative
Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling
task. 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 handling
coordination structures, our OpenIE system also incorporates a new coordination
analyzer built with the same IGL architecture. This IGL based coordination
analyzer helps our OpenIE system handle complicated coordination structures,
while also establishing a new state of the art on the task of coordination
analysis, with a 12.3 pts improvement in F1 over previous analyzers. Our OpenIE
system, OpenIE6, beats the previous systems by as much as 4 pts in F1, while
being much faster.
