For example, word embeddings tend to be consistent across language variations (Aldarmaki, Mohan, and Diab 2018), whereas multilingual vector spaces have more difficulty in representing individual words (such as, e.g., homographs with unrelated senses and phrasal verbs) because of their different usage distributions.
using such words in the alignment dictionary may undermine the mapping (Aldarmaki and Diab 2019).