4 citations · 8 across the 5 of their papers we have counts for
7 papers
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model
Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang +1
Recently, the problem of robustness of pre-trained language models (PrLMs) has received increasing research interest. Latest studies on adversarial attacks achieve high attack succ…
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation
Ke Wang, Yangbin Shi, Jiayi Wang +3
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a blac…
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice
Rongzhou Bao, Jiayi Wang, Hai Zhao
Pre-trained contextualized language models (PrLMs) have led to strong performance gains in downstream natural language understanding tasks. However, PrLMs can still be easily foole…
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…
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing
Jiayi Wang, Ke Wang, Niyu Ge +3
With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine tran…
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System
Kai Fan, Jiayi Wang, Bo Li +4
The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are,…