Adversarial Examples for Natural Language Classification Problems

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Analysis Methods in Neural Language Processing: A Survey (2018, methodology): “Others ensured syntactic or semantic coherence in different ways, such as filtering replacements by word similarity or sentence similarity (Alzantot et al., 2018; Kuleshov et al., 2018), or by using synonyms and other word lists (Samanta and Mehta, 2017; Yang et al.”
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models (2020, background): “Similarly, some work has focused on text classification (Kuleshov et al., 2018; Liang et al., 2018), but in the white-box setting as compared to our framework is proposed in the black-box setting.”
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment (2019, background): “2018) that iterates through every word in the sentence and find its perturbation, and (Kuleshov et al. 2018) that uses”
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020, methodology): “(Kuleshov et al., 2018) Untargeted Classification Thought vector encoding cosine similarity, Language model similarity probability Counter-fitted word embedding swap Greedy word swap”
Generating Natural Language Adversarial Examples (2018, background): “In addition, while preparing our submission, we became aware of recent work which target a similar contribution (Kuleshov et al., 2018; Ebrahimi et al., 2018).”