update bibtex

README.md

@@ -440,7 +440,7 @@ A: 我们集成了RoBERTa和BERT-wwm的优点，对两者进行了一个自然
 
 
 ## 引用
-如果本目录中的内容对你的研究工作有所帮助，请在文献中引用下述技术报告：
+如果本目录中的内容对你的研究工作有所帮助，欢迎在论文中引用下述技术报告：
 https://arxiv.org/abs/1906.08101
 ```
 @article{chinese-bert-wwm,
@@ -451,6 +451,15 @@ https://arxiv.org/abs/1906.08101
  }
 ```
 
+或 https://arxiv.org/abs/2004.13922
+```
+@article{cui-2020-revisiting,
+  title={Revisiting Pre-Trained Models for Chinese Natural Language Processing},
+  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Wang, Shijin and Hu, Guoping},
+  journal={arXiv preprint arXiv:2004.13922},
+  year={2020}
+}
+```
 
 ## 致谢
 第一作者部分受到[**谷歌TensorFlow Research Cloud**](https://www.tensorflow.org/tfrc)计划资助。

README_EN.md

@@ -401,7 +401,7 @@ A: integrate whole word masking (wwm) into RoBERTa model, specifically:
 3) directly use the data generated by `max_len=512` (but not from `max_len=128` for several steps then `max_len=512`)
 4) extended training steps (1M steps)
 
-## Reference
+## Citation
 If you find the technical report or resource is useful, please cite the following technical report in your paper.
 https://arxiv.org/abs/1906.08101
 ```
@@ -413,6 +413,16 @@ https://arxiv.org/abs/1906.08101
  }
 ```
 
+or https://arxiv.org/abs/2004.13922
+```
+@article{cui-2020-revisiting,
+  title={Revisiting Pre-Trained Models for Chinese Natural Language Processing},
+  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Wang, Shijin and Hu, Guoping},
+  journal={arXiv preprint arXiv:2004.13922},
+  year={2020}
+}
+```
+
 ## Disclaimer
 **This is NOT a project by Google official. Also, this is NOT an official product by HIT and iFLYTEK.**
 The experiments only represent the empirical results in certain conditions and should not be regarded as the nature of the respective models. The results may vary using different random seeds, computing devices, etc.