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

arXiv: https://arxiv.org/abs/2010.03147 · HTML: https://mlsanity.com/paper/2010.03147/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression](https://mlsanity.com/paper/2404.00489/) (2024)
- [Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction](https://mlsanity.com/paper/2305.13981/) (2023)
- [Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents](https://mlsanity.com/paper/2302.06854/) (2023)
- [Cross-lingual Inference with A Chinese Entailment Graph](https://mlsanity.com/paper/2203.06264/) (2022)
- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion](https://mlsanity.com/paper/2104.08741/) (2021)
- [A Library Perspective on Nearly-Unsupervised Information Extraction Workflows in Digital Libraries](https://mlsanity.com/paper/2205.00716/) (2022)
- [The Exploration of Knowledge-Preserving Prompts for Document Summarisation](https://mlsanity.com/paper/2301.11719/) (2023)
- [Towards Learning Through Open-Domain Dialog](https://mlsanity.com/paper/2202.03040/) (2022)
- [BEKG: A Built Environment Knowledge Graph](https://mlsanity.com/paper/2211.02864/) (2022)

## Abstract (arXiv)

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.