Released code for our EMNLP22 paper: UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction.
Join the Discord if there are any questions.
UniRel
class in predict.py
for easy inference and a checkpoint trained on nyt (multi-token) for trying.UniRel is implemented with Python == 3.8
and pytorch == 1.7.1
, Other main requirments are:
The detail requirments could be found at requirements.txt
We obtain the data from TPLinker, please kindly refer to TPLinker officail repository. Change two filename of the download data:
train_data.json
-> train_split.json
test_triples.json
-> test_data.json
You can also download the data from here
We use the bert-base-cased
model from Huggingface, you can download it by following their instrcution or let Transformers to automatically download. After that, place the files at the root directory of the project (./bert-base-cased
).
I provided a checkpoint for trying predict
. You can download here.
All parameter are listed in the script run_nyt.sh
and run_webnlg.sh
. By run with command bash run_nyt.sh
can do train and evaluation.
@inproceedings{tang-etal-2022-unirel,
title = "{U}ni{R}el: Unified Representation and Interaction for Joint Relational Triple Extraction",
author = "Tang, Wei and
Xu, Benfeng and
Zhao, Yuyue and
Mao, Zhendong and
Liu, Yifeng and
Liao, Yong and
Xie, Haiyong",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.477",
pages = "7087--7099",
}
Have a nice day.