They are.

There was a SemEval competition about having vectors that are both accurate for 5 individual languages and aligned between all the pairs of languages [1], where Numberbatch was the first-place system [2].

The fastText_multilingual system does have something going on, that it can discover multilingually aligned vectors after the fact for a system that was previously separate for each language, and leaves it "the same system" in a sense. But incorporating the ConceptNet knowledge graph also gives you a multilingually aligned system.

[1] http://alt.qcri.org/semeval2017/task2/

[2] https://blog.conceptnet.io/2017/03/02/how-luminoso-made-conc...

Cool rspeer, we will check NumberBatch out! Yes, as you say, our goal here is to show you can get something for nothing from pre-trained embeddings (I can learn the 78 matrices on my macbook in about ten minutes...).

The alignment procedure is completely agnostic to what word vectors you use. It only requires that cosine similarity is a good distance metric. We used fastText because there were lots of open-sourced vectors available...