IPED: An Implicit Perspective for Relational Triple Extraction based on
  Diffusion Model
Jianli Zhao Changhao Xu Bin Jiang
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.