Improving Distantly Supervised Relation Extraction by Natural Language Inference

TL;DR

This paper introduces DSRE-NLI, a framework that enhances distantly supervised relation extraction by leveraging natural language inference and automatic pattern mining, significantly boosting performance on benchmark datasets.

Contribution

The work proposes a novel NLI-based approach with semi-automatic relation verbalization and data consolidation strategies to improve distantly supervised relation extraction.

Findings

Achieves up to 7.73% F1 improvement over state-of-the-art methods.

Effectively enriches training data with automatically mined textual patterns.

Demonstrates significant performance gains on benchmark datasets.

Abstract

To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance. In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to provide indirect supervision and further consolidates the distant annotations to benefit multi-classification RE models. The NLI-based indirect supervision acquires only one relation verbalization template from humans as a semantically general template for each relationship, and then the template set is enriched by high-quality textual patterns automatically mined from the distantly annotated corpus. With two simple and effective data consolidation strategies, the quality of training data is substantially improved. Extensive experiments demonstrate that the proposed framework significantly improves the SOTA performance (up to 7.73\% of F1) on distantly supervised RE benchmark datasets.