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

TL;DR

OpenIE6 introduces an innovative iterative grid labeling approach for open information extraction, achieving state-of-the-art accuracy and 10x faster processing by treating extraction as a 2-D grid labeling task and incorporating coordination analysis.

Contribution

The paper presents a novel Iterative Grid Labeling architecture for OpenIE that improves extraction quality and speed, and integrates a coordination analyzer for complex structure handling.

Findings

Achieves new state-of-the-art OpenIE performance.

Extracts data 10 times faster than previous systems.

Significantly improves coordination analysis accuracy.

Abstract

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.