Papers by Yiting Lu
Grammatical Error Correction Systems for Automated Assessment: Are They Susceptible to Universal Adversarial Attacks? (2022.aacl-main)
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| Challenge: | With advances in deep learning, GEC systems are susceptible to adversarial attacks, in which a small change at the input can cause large undesired changes at the output. |
| Approach: | They propose to use a concatenative universal attack to deceive the system into not correcting grammatical errors to create the perception of higher language ability. |
| Outcome: | The proposed attack can deceive the system into not correcting (concealing) grammatical errors to create the perception of higher language ability. |