Under the hood: Multilingual embeddings | Engineering Blog | Facebook Code(About) With this technique, embeddings for every language exist in the same vector space, and maintain the property that words with similar meanings (regardless of language) are close together in vector space
> To train these multilingual word embeddings, we first trained separate embeddings for each language using fastText and a combination of data from Facebook and Wikipedia. We then used dictionaries to project each of these embedding spaces into a common space (English). The dictionaries are automatically induced from parallel data — meaning data sets that consist of a pair of sentences in two different languages that have the same meaning — which we use for training translation systems.
GitHub - Babylonpartners/fastText_multilingual: Multilingual word vectors(About) Aligning the fastText vectors of 78 languages.
> In a recent paper at ICLR 2017, we showed how the SVD can be used to learn a linear transformation (a matrix), which aligns monolingual vectors from two languages in a single vector space. In this repository we provide 78 matrices, which can be used to align the majority of the fastText languages in a single space.
[How to align two vector spaces for myself!](https://github.com/Babylonpartners/fastText_multilingual/blob/master/align_your_own.ipynb)