Downstream Model Design of Pre-trained Language Model for Relation Extraction Task

TL;DR

This paper proposes a novel downstream model architecture with a specialized loss function for pre-trained language models to improve supervised relation extraction, significantly outperforming existing baselines.

Contribution

It introduces a new network architecture and loss function tailored for PLMs to better handle complex relations in relation extraction tasks.

Findings

Significant performance improvements over baseline models

Effective handling of complicated relations in datasets

Validated on multiple public datasets

Abstract

Supervised relation extraction methods based on deep neural network play an important role in the recent information extraction field. However, at present, their performance still fails to reach a good level due to the existence of complicated relations. On the other hand, recently proposed pre-trained language models (PLMs) have achieved great success in multiple tasks of natural language processing through fine-tuning when combined with the model of downstream tasks. However, original standard tasks of PLM do not include the relation extraction task yet. We believe that PLMs can also be used to solve the relation extraction problem, but it is necessary to establish a specially designed downstream task model or even loss function for dealing with complicated relations. In this paper, a new network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction. Experiments have shown that our method significantly exceeded the current optimal baseline models across multiple public datasets of relation extraction.