activity
20202022
most citedDefending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice

4 citations · 5 across the 4 of their papers we have counts for

collaborators
Showing 2020Show all

1 paper · 1 filter

cs.CL2020★ 1 cited

Enhancing Pre-trained Language Model with Lexical Simplification

Rongzhou Bao, Jiayi Wang, Zhuosheng Zhang +1

For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given…