论文标题
为TableQa生成语义上有效的对抗性问题
Generating Semantically Valid Adversarial Questions for TableQA
论文作者
论文摘要
对问题答案系统对表格数据（TableQA）的对抗性攻击可以帮助评估他们可以在何种程度上使用表理解自然语言问题和原因。但是，很难产生自然语言对抗性问题，因为即使是单个角色交换也可能导致人类感知的巨大语义差异。在本文中，我们提出了Sage（语义上有效的对抗发电机），这是TableQQA White-Box攻击的Wasserstein序列到序列模型。为了保留原始问题的含义，我们将最低风险培训用于明喻和实体dilexicalization。我们使用Gumbel-Softmax来纳入端到端培训的对抗性损失。我们的实验表明，Sage在语义有效性和流利度上的现有本地攻击模型在实现良好的攻击成功率的同时都优于现有的本地攻击模型。最后，我们证明了使用SAGE增强数据的对抗性训练可以改善TableQA系统的性能和稳健性。
Adversarial attack on question answering systems over tabular data (TableQA) can help evaluate to what extent they can understand natural language questions and reason with tables. However, generating natural language adversarial questions is difficult, because even a single character swap could lead to huge semantic difference in human perception. In this paper, we propose SAGE (Semantically valid Adversarial GEnerator), a Wasserstein sequence-to-sequence model for TableQA white-box attack. To preserve meaning of original questions, we apply minimum risk training with SIMILE and entity delexicalization. We use Gumbel-Softmax to incorporate adversarial loss for end-to-end training. Our experiments show that SAGE outperforms existing local attack models on semantic validity and fluency while achieving a good attack success rate. Finally, we demonstrate that adversarial training with SAGE augmented data can improve performance and robustness of TableQA systems.
