9 citations · 14 across the 9 of their papers we have counts for
7 papers · 1 filter
fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations
Jinfeng Li, Yuefeng Chen, Xiangyu Liu +3
Pre-trained language models (PLMs) have revolutionized both the natural language processing research and applications. However, stereotypical biases (e.g., gender and racial discri…
A Survey on Knowledge-Enhanced Pre-trained Language Models
Chaoqi Zhen, Yanlei Shang, Xiangyu Liu +3
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs…
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Zihan Zhang, Jinfeng Li, Ning Shi +6
Despite of the superb performance on a wide range of tasks, pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts. In this paper, we present RoC…
SemAttack: Natural Textual Attacks via Different Semantic Spaces
Boxin Wang, Chejian Xu, Xiangyu Liu +2
Recent studies show that pre-trained language models (LMs) are vulnerable to textual adversarial attacks. However, existing attack methods either suffer from low attack success rat…
Incorporating External POS Tagger for Punctuation Restoration
Ning Shi, Wei Wang, Boxin Wang +3
Punctuation restoration is an important post-processing step in automatic speech recognition. Among other kinds of external information, part-of-speech (POS) taggers provide inform…
Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation
Jinfeng Li, Tianyu Du, Xiangyu Liu +3
Despite that deep neural networks (DNNs) have achieved enormous success in many domains like natural language processing (NLP), they have also been proven to be vulnerable to malic…
Revisit Systematic Generalization via Meaningful Learning
Ning Shi, Boxin Wang, Wei Wang +2
Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity,…