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Grammatical Error Correction (GEC) systems perform a sequence-to-sequencetask, where an input word sequence containing grammatical errors, is correctedfor these errors by the GEC system to output a grammatically correct wordsequence. With the advent of deep learning methods, automated GEC systems havebecome increasingly popular. For example, GEC systems are often used on speechtranscriptions of English learners as a form of assessment and feedback - thesepowerful GEC systems can be used to automatically measure an aspect of acandidate's fluency. The count of \textit{edits} from a candidate's inputsentence (or essay) to a GEC system's grammatically corrected output sentenceis indicative of a candidate's language ability, where fewer edits suggestbetter fluency. The count of edits can thus be viewed as a \textit{fluencyscore} with zero implying perfect fluency. However, although deep learningbased GEC systems are extremely powerful and accurate, they are susceptible toadversarial attacks: an adversary can introduce a small, specific change at theinput of a system that causes a large, undesired change at the output. Whenconsidering the application of GEC systems to automated language assessment,the aim of an adversary could be to cheat by making a small change to agrammatically incorrect input sentence that conceals the errors from a GECsystem, such that no edits are found and the candidate is unjustly awarded aperfect fluency score. This work examines a simple universal substitutionadversarial attack that non-native speakers of English could realisticallyemploy to deceive GEC systems used for assessment.

Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment