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

arXiv: https://arxiv.org/abs/2004.03786 · HTML: https://mlsanity.com/paper/2004.03786/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey](https://mlsanity.com/paper/2410.07824/) (2024)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction](https://mlsanity.com/paper/2111.09035/) (2021)
- [Modeling Multi-Granularity Hierarchical Features for Relation Extraction](https://mlsanity.com/paper/2204.04437/) (2022)
- [Multiple Relations Classification using Imbalanced Predictions Adaptation](https://mlsanity.com/paper/2309.13718/) (2023)

## Abstract (arXiv)

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.