4 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.PL2021★ 1 cited
How could Neural Networks understand Programs?
Dinglan Peng, Shuxin Zheng, Yatao Li +3
Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniq…
cs.CL2020★ 4 cited
Taking Notes on the Fly Helps BERT Pre-training
Qiyu Wu, Chen Xing, Yatao Li +3
How to make unsupervised language pre-training more efficient and less resource-intensive is an important research direction in NLP. In this paper, we focus on improving the effici…