GAP: A novel Generative context-Aware Prompt-tuning method for relation extraction

The code of this repository is constantly being updated...

Our code is based on the KnowPrompt .

Our code consists of three crucial modules:

1. A pretrained prompt generator module that extracts or generates the relation triggers from the context and embeds them into the prompt tokens;
2. An in-domain adaptive pretraining module that further trains the Pretrained Language Models (PLMs) to promote the adaptability of the model;
3. A joint contrastive loss that prevents PLMs from generating unrelated content and optimizes our model more effectively.

Please look forward to it!

Introduction

This repository is used in our paper:

《GAP: A novel Generative context-Aware Prompt-tuning method for Relation Extraction》

Zhenbin Chen, Zhixin Li, Ying Huang, Zhenjun Tang.

Please cite our paper and kindly give a star for this repository if you use this code.

Usage

Semeval

sh ./scripts/semeval.sh

TACRED

sh ./scripts/tacred.sh

TACREV

sh ./scripts/tacrev.sh

Re-TACRED

sh ./scripts/retacred.sh

Citation

@article{chen2024gap,
  title={GAP: A novel Generative context-Aware Prompt-tuning method for relation extraction},
  author={Chen, Zhenbin and Li, Zhixin and Zeng, Yufei and Zhang, Canlong and Ma, Huifang},
  journal={Expert Systems with Applications},
  volume={248},
  pages={123478},
  year={2024},
  publisher={Elsevier}
}