OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models

By Xiao Liu, Da Yin, Kai Su, Kan Wu, Hongxia Yang, Jie Tang. 2021

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Han
2. Xiao Liu
3. Ning Ding
4. Zhiyuan Liu
5. Minlie Huang
6. Yuxian Gu
7. Xipeng Qiu
8. Zhengyan Zhang

Citations in the corpus

• Pre-Trained Models: Past, Present and Future (2021, methodology): “In P-tuning (Liu et al., 2021b), the authors discover that the better prompts lie in continuous embedding space, rather than discrete space.”
• AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing (2021, methodology): “For example, there are no benchmarks to evaluate a) the progress in compact pretrained models b) the robustness of pretrained models c) PTLMs specific to social media as well as specific to other domains like Academic.”