The surge of state-of-the-art Transformer-based models has undoubtedly pushed the limits of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the underexplored task of Natural Language Inference (NLI), since models trained on popular well-suited datasets are susceptible to adversarial attacks, allowing subtle input interventions to mislead the model. In this work, we validate the usage of natural language explanation as a model-agnostic defence strategy through extensive experimentation: only by fine-tuning a classifier on the explanation rather than premise-hypothesis inputs, robustness under various adversarial attacks is achieved in comparison to explanation-free baselines. Moreover, since there is no standard strategy of testing the semantic validity of the generated explanations, we research the correlation of widely used language generation metrics with human perception, in order for them to serve as a proxy towards robust NLI models. Our approach is resource-efficient and reproducible without significant computational limitations.

本研究针对自然语言推理（NLI）中模型容易受到对抗攻击的缺陷，提出了一种利用自然语言解释作为模型无关的防御策略。通过大量实验，我们证明了通过针对解释进行分类器微调可以显著提高模型在面对各种对抗攻击时的鲁棒性。此外，我们还探讨了常用语言生成指标与人类感知的相关性，为生成的解释提供了验证标准，助力构建更强大的NLI模型。

利用解释增强自然语言推理中的对抗鲁棒性