IPED: An Implicit Perspective for Relational Triple Extraction based on Diffusion Model
This work addresses a fundamental task in information extraction for applications like knowledge graph construction, but it appears incremental as it builds on existing table-filling frameworks.
The paper tackles the problem of redundant information and incomplete triple recognition in relational triple extraction by proposing IPED, an implicit perspective based on a diffusion model, which achieves state-of-the-art performance with superior inference speed and low computational complexity on two popular datasets.
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.