This repository contains the implementation of the NAACL 2021 paper "Towards Zero-Shot Relation Extraction with Attribute Representation Learning".
You can download the datasets employed in our work from the following link:
and place them to the /data folder.
ZS-BERT/
├── model
├── model.py
├── data_helper.py
├── evaluation.py
├── train_wiki.py
└── train_fewrel.py
└── resources/
├── property_list.html
└── data/
├── wiki_train_new.json
└── fewrel_all.json
python >= 3.6 torch >= 1.4.0 or simply run:
pip install -r requirements.txt
If you wish to train on the wiki dataset, run:
python3 train_wiki.py --seed 300 --n_unseen 10 --gamma 7.5 --alpha 0.4 --dist_func 'inner' --batch_size 4 --epochs 10
Otherwise to train on FewRel dataset, you can run:
python3 train_fewrel.py --seed 300 --n_unseen 10 --gamma 7.5 --alpha 0.4 --dist_func 'inner' --batch_size 4 --epochs 10
inside the /model folder.
If you use the code, we appreciate it if you cite the following paper:
@inproceedings{chen2021zsbert,
title={ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning},
author={Chih-Yao Chen and Cheng-Te Li},
booktitle={Proceedings of 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2021)},
year={2021}
}