Expanding automatic machine translation to more languages

What this post added

This post details the expansion of Facebook's automatic machine translation (NMT) services to include 24 new languages. It describes the technical challenges of low-resource language translation, including lack of training data and the need for fast training. The post outlines three key strategies employed: 1) Increasing labeled in-domain data by manually labeling millions of words in 25 languages, showing an average BLEU score increase of +7.2. 2) Exploring semisupervised NMT techniques like back-translation (average +2.5 BLEU), copy-target (average +2.7 BLEU), and mining translations from multilingual webpages (average +0.4 BLEU), along with in-domain data up-sampling for improved fluency and relevance. 3) Utilizing multilingual modeling by combining related language directions, which improved BLEU scores by an average of +4.6. The post also mentions the use of PyTorch Translate, ONNX, and Caffe2 environments for training and production.