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.
among the main areas where multilingual and crosslingual resources and approaches are solicited, there are of course machine translation (Cho et al. 2014; Luong, Pham, and Manning 2015), crosslingual document categorization (Kocisky, Hermann, and Blunsom 2014; Gouws, Bengio, and Corrado 2015), and sentiment analysis (Tang et al. 2014).