  Current Machine Translation (MT) models still struggle with more challenging
input, such as noisy data and tail-end words and phrases. Several works have
addressed this robustness issue by identifying specific categories of noise and
variation then tuning models to perform better on them. An important yet
under-studied category involves minor variations in nuance (non-typos) that
preserve meaning w.r.t. the target language. We introduce and formalize this
category as Natural Asemantic Variation (NAV) and investigate it in the context
of MT robustness. We find that existing MT models fail when presented with NAV
data, but we demonstrate strategies to improve performance on NAV by
fine-tuning them with human-generated variations. We also show that NAV
robustness can be transferred across languages and find that synthetic
perturbations can achieve some but not all of the benefits of organic NAV data.
