E.g., if we perturb all characters in a sentence (p = 1.0), the SELMo embeddings of the perturbed sentence are in 1% of the cases more similar to the original sentence whereas this is the case for more than 34-39% for VELMo.
vELMo embeddings better capture similarity between visually similar words.