Chinese Kowledge-enhanced BERT (CKBERT)
Knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community to support various language understanding applications.
For Chinese natural language processing, we provide three Chinese Kowledge-enhanced BERT (CKBERT) models named pai-ckbert-bert-zh, pai-ckbert-large-zh and pai-ckbert-huge-zh, from our EMNLP 2022 paper named Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training.
This repository is developed based on the EasyNLP framework: https://github.com/alibaba/EasyNLP
Citation
If you find the resource is useful, please cite the following papers in your work.
- For the EasyNLP framework:
@article{easynlp,
title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing},
author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei},
publisher = {arXiv},
url = {https://arxiv.org/abs/2205.00258},
year = {2022}
}
- For CKBERT:
@article{ckbert,
title = {Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training},
author = {Zhang, Taolin and Dong, Junwei and Wang, Jianing and Wang, Chengyu and Wang, An and Liu, Yinghui and Huang, Jun and Li, Yong and He, Xiaofeng},
publisher = {EMNLP},
url = {https://arxiv.org/abs/2210.05287},
year = {2022}
}
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