Relational triple extraction is a fundamental task in the field of
information extraction, and a promising framework based on table filling has
recently gained attention as a potential baseline for entity relation
extraction. However, inherent shortcomings such as redundant information and
incomplete triple recognition remain problematic. To address these challenges,
we propose an Implicit Perspective for relational triple Extraction based on
Diffusion model (IPED), an innovative approach for extracting relational
triples. Our classifier-free solution adopts an implicit strategy using block
coverage to complete the tables, avoiding the limitations of explicit tagging
methods. Additionally, we introduce a generative model structure, the
block-denoising diffusion model, to collaborate with our implicit perspective
and effectively circumvent redundant information disruptions. Experimental
results on two popular datasets demonstrate that IPED achieves state-of-the-art
performance while gaining superior inference speed and low computational
complexity. To support future research, we have made our source code publicly
available online.

基于扩散模型的隐形角度关系三元组抽取是一种创新方法，旨在解决关系三元组抽取中存在的冗余信息和不完整识别等挑战，实验证明其达到了最先进的性能，并具有优越的推理速度和低计算复杂度。

基于扩散模型的隐性检测视角下的关系三元组抽取

IPED: An Implicit Perspective for Relational Triple Extraction based on  Diffusion Model

Table filling based relational triple extraction methods are attracting
growing research interests due to their promising performance and their
abilities on extracting triples from complex sentences. However, this kind of
methods are far from their full potential because most of them only focus on
using local features but ignore the global associations of relations and of
token pairs, which increases the possibility of overlooking some important
information during triple extraction. To overcome this deficiency, we propose a
global feature-oriented triple extraction model that makes full use of the
mentioned two kinds of global associations. Specifically, we first generate a
table feature for each relation. Then two kinds of global associations are
mined from the generated table features. Next, the mined global associations
are integrated into the table feature of each relation. This
"generate-mine-integrate" process is performed multiple times so that the table
feature of each relation is refined step by step. Finally, each relation's
table is filled based on its refined table feature, and all triples linked to
this relation are extracted based on its filled table. We evaluate the proposed
model on three benchmark datasets. Experimental results show our model is
effective and it achieves state-of-the-art results on all of these datasets.
The source code of our work is available at: this https URL

本文提出了一种全局特征导向的三元组抽取模型（GTETEM），用于从复杂的句子中提取三元组，并利用全局关联进一步提升抽取效果。该模型引入新的表格填充策略，将全局关联信息与局部特征结合，使得每个关系的表格特征可以逐步细化，最终实现了表格填充和三元组提取。实验结果表明，该模型在三个基准数据集上取得了最优效果。