Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence

TL;DR

This paper introduces a dual pointer network model with multi-head attention for efficiently extracting multiple relations between entities in a sentence, outperforming previous methods on standard datasets.

Contribution

The paper presents a novel dual pointer network architecture that simultaneously captures multiple relations in a sentence, improving extraction accuracy over existing models.

Findings

Achieved 80.8% F1-score on ACE-2005 corpus

Achieved 78.3% F1-score on NYT corpus

Outperformed previous relation extraction models

Abstract

Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations using a forward object decoder. Then, it finds 1-to-n subject-object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus and an F1-score of 78.3% for the NYT corpus.