fastText_multilingual

Why we included this project

The repository ships 78 precomputed matrices from the ICLR 2017 paper on offline bilingual word vectors, aligning Facebook's monolingual fastText embeddings into one shared space. A word and its translation in another language end up close together, while the original monolingual similarity relationships stay untouched, so single-language results remain exactly the same. The authors showed the alignment even transfers to language pairs that never appeared in the training dictionaries, predicting translations between unseen pairs. A bundled notebook walks through learning your own alignment matrices, which makes this a genuine teaching resource for embedding alignment. Note that the project is no longer actively maintained, so treat it as a reference implementation rather than a dependency for production systems.