论文标题
EFSG：进化欺骗句子生成器
EFSG: Evolutionary Fooling Sentences Generator
论文作者
论文摘要
大型的预训练的语言表示模型（LMS）最近在许多NLP任务中收集了许多成功。 在2018年，伯特（Bert）和后来的继任者（例如罗伯塔（Roberta））获得了最先进的结果，从而实现了经典的基准任务，例如胶水基准。 之后，已经发表了有关对抗性攻击的作品，以测试其概括和鲁棒性。 在这项工作中，我们设计了一种进化欺骗句子生成器（EFSG），这是一种使用进化方法构建的模型和任务不合时宜的对抗攻击算法，以生成用于二进制分类任务的假阳性句子。 我们成功地将EFSG应用于COLA和MRPC任务，比较Bert和Roberta，以比较性能。结果证明了最先进的LMS中存在弱点。 我们最终测试了对EFSG的数据增强防御方法，在原始数据集上测试时，获得了更强的改进模型，而没有损失准确性。
Large pre-trained language representation models (LMs) have recently collected a huge number of successes in many NLP tasks. In 2018 BERT, and later its successors (e.g. RoBERTa), obtained state-of-the-art results in classical benchmark tasks, such as GLUE benchmark. After that, works about adversarial attacks have been published to test their generalization proprieties and robustness. In this work, we design Evolutionary Fooling Sentences Generator (EFSG), a model- and task-agnostic adversarial attack algorithm built using an evolutionary approach to generate false-positive sentences for binary classification tasks. We successfully apply EFSG to CoLA and MRPC tasks, on BERT and RoBERTa, comparing performances. Results prove the presence of weak spots in state-of-the-art LMs. We finally test adversarial training as a data augmentation defence approach against EFSG, obtaining stronger improved models with no loss of accuracy when tested on the original datasets.
