论文标题
Adv-Bert：伯特在拼写错误上并不强大！在伯特上生成自然对抗样品
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
论文作者
论文摘要
越来越多的文献声称深度神经网络的脆弱性在处理恶意创造的对抗性例子中。但是，尚不清楚模型在\ textit {自然而非恶意}对抗实例的现实场景中如何执行。这项工作系统地探讨了NLP中最先进的变压器式模型BERT的鲁棒性，以处理嘈杂的数据，尤其是在键入键盘时出现的错误。关于情感分析和问题回答基准的密集实验表明：（i）用各种句子的单词打字不会平等影响。信息词中的错别字会导致更严重的损害； （ii）与插入，删除等相比，错误类型是最具破坏因素。 （iii）人类和机器在识别对抗性攻击方面有不同的重点。
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, however, how the models will perform in realistic scenarios where \textit{natural rather than malicious} adversarial instances often exist. This work systematically explores the robustness of BERT, the state-of-the-art Transformer-style model in NLP, in dealing with noisy data, particularly mistakes in typing the keyboard, that occur inadvertently. Intensive experiments on sentiment analysis and question answering benchmarks indicate that: (i) Typos in various words of a sentence do not influence equally. The typos in informative words make severer damages; (ii) Mistype is the most damaging factor, compared with inserting, deleting, etc.; (iii) Humans and machines have different focuses on recognizing adversarial attacks.
