Relation-Guided Few-Shot Relational Triple Extraction

This code is for SIGIR 2022 short paper "Relation-Guided Few-Shot Relational Triple Extraction".

Overview

In this work, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. It first detects relations occurred in a sentence and then extracts the corresponding head/tail entities of the detected relations. To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relation-relevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities. Experimental results show that our model outperforms previous work by an absolute gain (18.98%, 28.85% in F1 in two few-shot settings).

You can find the paper in paper directory or download it from here.

Requirements

pytorch=1.7.1
cudatoolkit=10.2
transformers=3.5.1
gitpython=3.1.11

NOTE: Different versions of packages (such as pytorch, transformers, etc.) may lead to different results from the paper. However, the trend should still hold no matter what versions of packages you use.

Usage

1. Training model

python main.py

After training, this script will evaluate the model automatically. The best model will be saved in checkpoint directory.

All hyper-parameters are listed in config.py file. You can change it to conduct more experiments.

2. Evaluation

python main.py --model=relate --trainN=5 --evalN=5 --K=5 --Q=1 --load_ckpt="your_checkpoint_name_saved_in_checkpoint_dir" --test

Citation

@inproceedings{cong2022RelATE,
 author = {Cong, Xin and Sheng, Jiawei and Cui, Shiyao and Yu, Bowen and Liu, Tingwen and Wang, Bin},
 booktitle = {Proc. of SIGIR},
 title = {Relation-Guided Few-Shot Relational Triple Extraction},
 year = {2022}
}