There is growing evidence that the classical notion of adversarial robustness
originally introduced for images has been adopted as a de facto standard by a
large part of the NLP research community. We show that this notion is
problematic in the context of NLP as it considers a narrow spectrum of
linguistic phenomena. In this paper, we argue for semantic robustness, which is
better aligned with the human concept of linguistic fidelity. We characterize
semantic robustness in terms of biases that it is expected to induce in a
model. We study semantic robustness of a range of vanilla and robustly trained
architectures using a template-based generative test bed. We complement the
analysis with empirical evidence that, despite being harder to implement,
semantic robustness can improve performance %gives guarantees for on complex
linguistic phenomena where models robust in the classical sense fail.

本文认为在自然语言处理领域中使用的对抗性鲁棒性概念是有问题的，并提出了更符合语言语义的 “语义鲁棒性” 概念。在使用基于模板的生成测试平台和多种模型进行实验后，发现语义鲁棒性不但可以提高性能，在处理复杂的语言现象时也能取得一定的保证。