Pre-trained transformers are popular in state-of-the-art dialogue generation (DG) systems. Such language models are, however, vulnerable to various adversarial samples as studied in traditional tasks such as text classification, which inspires our curiosity about their robustness in DG systems. One main challenge of attacking DG models is that perturbations on the current sentence can hardly degrade the response accuracy because the unchanged chat histories are also considered for decision-making. Instead of merely pursuing pitfalls of performance metrics such as BLEU, ROUGE, we observe that crafting adversarial samples to force longer generation outputs benefits attack effectiveness -- the generated responses are typically irrelevant, lengthy, and repetitive. To this end, we propose a white-box multi-objective attack method called DGSlow. Specifically, DGSlow balances two objectives -- generation accuracy and length, via a gradient-based multi-objective optimizer and applies an adaptive searching mechanism to iteratively craft adversarial samples with only a few modifications. Comprehensive experiments on four benchmark datasets demonstrate that DGSlow could significantly degrade state-of-the-art DG models with a higher success rate than traditional accuracy-based methods. Besides, our crafted sentences also exhibit strong transferability in attacking other models.

翻译：预训练Transformer模型在先进的对话生成系统中广泛使用。然而，这类语言模型如同在文本分类等传统任务中所研究的，容易受到各种对抗样本的攻击，这引发了我们对对话生成系统中模型鲁棒性的好奇。攻击对话生成模型的一个主要挑战在于，对当前句子的扰动难以降低响应准确率，因为未改变的聊天历史同样会被用于决策。我们观察到，除了仅仅追求BLEU、ROUGE等性能指标的下降外，制造对抗样本以迫使生成更长的输出有助于提升攻击效果——生成的响应通常不相关、冗长且重复。为此，我们提出一种名为DGSlow的白盒多目标攻击方法。具体而言，DGSlow通过基于梯度的多目标优化器平衡生成准确率和生成长度这两个目标，并应用自适应搜索机制以仅少量修改即可迭代生成对抗样本。在四个基准数据集上的全面实验表明，DGSlow能够显著降低先进对话生成模型的性能，且成功率高于传统的基于准确率的方法。此外，我们生成的句子在攻击其他模型时也表现出强可迁移性。