Kyle Wiggers wrote a feature for Venture Beat on Facebook’s new M2M-100 model — a machine translation model that, unlike e.g. Google Translate does for many language pairs, does not use English as go-between. Instead of translating from A to English and then from English to B, it translates form A directly to B — which for 100 languages means there are nearly 10,000 combinations. The model was trained on 2200 of these combinations, and is a new state of the art (in terms of BLEU) for many non-English language pairs. The model has 15 billion parameters, continuing the trend that strength really is in numbers for NLP and MT models. FAIR has open-sourced M2M-100 at pytorch/fairseq.