no code implementations • 21 Jul 2022 • Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
However, we find that most existing textual adversarial examples are unnatural, which can be easily distinguished by both human and machine.
1 code implementation • Findings (ACL) 2022 • Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
We question the validity of current evaluation of robustness of PrLMs based on these non-natural adversarial samples and propose an anomaly detector to evaluate the robustness of PrLMs with more natural adversarial samples.
no code implementations • Findings (EMNLP) 2021 • Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words.
1 code implementation • 30 May 2021 • Rongzhou Bao, Jiayi Wang, Hai Zhao
In detail, we design an auxiliary anomaly detection classifier and adopt a multi-task learning procedure, by which PrLMs are able to distinguish adversarial input samples.
no code implementations • 1 Jan 2021 • Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Instead of too early fixing the linguistic unit input as nearly all previous work did, we propose a novel method that combines span-level information into the representations generated by PrLMs during fine-tuning phase for better flexibility.
no code implementations • 30 Dec 2020 • Rongzhou Bao, Jiayi Wang, Zhuosheng Zhang, Hai Zhao
By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical diversity, and therefore to improve the understandability of sentences.