Released code for our ACL23 paper: S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction
Training data can be found at ./data/, while generated synthetic data (either by ChatGPT or by finetuned GPT2) can be downloaded here:
https://drive.google.com/file/d/1-oPgKi3DhnpuFNAxAgOhqe0eec-8DR-r/view?usp=sharing
Following the paper setting, you should sample 10,000 for 1% setting, and 100,000 for 10% or 100% setting.
bash ./script/run_gen_iterative.sh
Note that this is the end-to-end script that integrates multiple process, including 1) training GPT2; 2) using GPT2 to generate synthetic samples; 3) the iterative process of distilling and two-stage trainig.
Cite us as:
@inproceedings{xu-etal-2023-s2ynre,
title = "{S}2yn{RE}: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction",
author = "Xu, Benfeng and
Wang, Quan and
Lyu, Yajuan and
Dai, Dai and
Zhang, Yongdong and
Mao, Zhendong",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.455",
doi = "10.18653/v1/2023.acl-long.455",
pages = "8186--8207",
}