Beyond synonym-swapping, several other types of adversarial attacks can be effective against conversation entailment models. Here are some examples of such attacks and potential defense strategies:
Grammatical Errors Injection: Introducing subtle grammatical errors or inconsistencies in the text can confuse the model and lead to incorrect predictions. Defending against this type of attack requires incorporating grammar checking mechanisms during training to encourage the model to learn robust linguistic patterns.
Semantic Drift: Altering the semantics of the text while preserving grammaticality can mislead the model into making incorrect entailment judgments. Defenses against semantic drift attacks involve training the model on diverse and semantically rich datasets to improve its understanding of context and meaning.
Contextual Deception: Providing contextually misleading information that contradicts the true entailment relationship can deceive the model. To defend against this attack, models can be trained with attention mechanisms that focus on relevant context and discard irrelevant information.
Syntactic Ambiguity: Exploiting syntactic ambiguities in the text to create multiple valid interpretations can lead to incorrect predictions. Defenses against syntactic ambiguity attacks involve training the model with syntactically diverse data and incorporating syntactic parsing techniques to disambiguate complex structures.
By incorporating a combination of data augmentation, robust training strategies, and diverse adversarial example generation techniques, conversation entailment models can be better equipped to handle a wide range of adversarial attacks and maintain performance across different domains.