High-performance neural language models have obtained state-of-the-art
results on a wide range of Natural Language Processing (NLP) tasks. However,
results for common benchmark datasets often do not reflect model reliability
and robustness when applied to noisy, real-world data. In this study, we design
and implement various types of character-level and word-level perturbation
methods to simulate realistic scenarios in which input texts may be slightly
noisy or different from the data distribution on which NLP systems were
trained. Conducting comprehensive experiments on different NLP tasks, we
investigate the ability of high-performance language models such as BERT,
XLNet, RoBERTa, and ELMo in handling different types of input perturbations.
The results suggest that language models are sensitive to input perturbations
and their performance can decrease even when small changes are introduced. We
highlight that models need to be further improved and that current benchmarks
are not reflecting model robustness well. We argue that evaluations on
perturbed inputs should routinely complement widely-used benchmarks in order to
yield a more realistic understanding of NLP systems robustness.

我们研究了在现实世界中输入文本可能有噪音或不同于 NLP 系统训练数据分布的情景，通过各种类型的字符级和单词级扰动方法来模拟这种情况，发现语言模型对输入扰动非常敏感，即使引入了很小的变化，其性能也会下降，需要进一步改进模型并对扰动输入进行评估以更加真实地了解 NLP 系统的鲁棒性。