Model paves way for faster, more efficient translations of more languages
MIT researchers have developed a novel “unsupervised” language translation model — meaning it runs without the need for human annotations and guidance — that could lead to faster, more efficient computer-based translations of far more languages. Translation systems from Google, Facebook, and Amazon require training models to look for patterns in millions of documents — such as legal and political documents, or news articles — that have been translated into various languages by humans. Given new words in one language, they can then find the matching words and phrases in the other language. But this translational data is time consuming and difficult to gather, and simply may not exist for many of the 7,000 languages spoken worldwide. Recently, researchers have been developing “monolingual” models that make translations between texts in two languages, but without direct translational information between the two. In a paper being presented this week at the Conference on Empirical Methods in Natural Language...