*Read Jaap’s article on TAUS here.
Recent blog posts
Nowadays, in one way or another, machine translation (MT) is part of our everyday lives. Most likely Google made that happen, about a decade ago, by launching Google Translate, a free instant online general-purpose translator allowing users to translate any text (words, phrases, documents, web pages) in different language directions.
The last significant breakthrough in the technology of statistical machine translation (SMT) was in 2005. That year, David Chiang published his famous paper on hierarchical translation models that allowed to significantly improve the quality of statistical MT between distant languages. Nowadays we are standing on the verge of an even more exciting moment in MT history: deep learning (DL) is taking MT towards much higher accuracy and finally brings human-like semantics to the translation process.
Neural Machine Translation (NMT) systems have achieved impressive results in many Machine Translation (MT) tasks in the past couple of years. This is mainly due to the fact that Neural Networks can solve non-linear functions, making NMT perfect for mimicking the linguistic rules followed by the human brain.
When I attended translation courses, I was assigned to write a commentary on George F. Will’s column Reading, Writing and Rationality on the Newsweek issue ofMarch 17, 1986.
Even then, with no Internet and television as the dominant media, students were urged to read.
In a recent NPR news piece Uber Plans To Kill Surge Pricing, Though Drivers Say It Makes Job Worth It, Jeff Schneider, engineering lead at Uber describes how they are using machine learning to hack the problem of supply and demand.