State-Of-The-Art Methods For Neural Machine Translation & Multilingual Tasks

The quality of machine translation produced by state-of-the-art models is already quite high and often requires only minor corrections from professional human translators. This is especially true for high-resource language pairs like English-German and English-French. So, the main focus of recent research studies in machine translation was on improving system performance for low-resource language pairs, where we have access to large monolingual corpora in each language but do not have sufficiently large parallel corpora. Facebook AI researchers seem to lead in this research area and have introduced several interesting solutions for low-resource machine translation during the last year. This includes augmenting the training data with back-translation, learning joint multilingual sentence representations, as well as extending BERT to a cross-lingual setting.