The corpus consists of original and translated texts in both directions and is designed as a multifunctional resource to be used in a number of disciplines such as corpus-based contrastive linguistic and translation studies, machine translation, computer-assisted translation, computer-assisted language learning and terminology extraction.
Google has shoveled vast financial and engineering resources into a collection of data mining and artificial intelligence systems, from speech recognition to machine translation to computer vision.
The general idea behind machine translation is that computers have the patience, stamina and speed to quickly parse through gigabytes of text, matching text terms with equivalent terms from an external vocabulary.
Machine translation – the use of computers to translate automatically among human languages – is an alluring prospect, one that for more than 50 years has fascinated researchers, inspired idealists and opportunists, and provoked unease among professional translators.
"This … demonstrates like never before the power of neural machine translation," says Yoshua Bengio, a computer scientist at the University of Montreal in Canada, who helped invent one of the critical components of the new system several years ago, but who was not involved in the current work.
Statistical machine translation -- in which computers essentially learn new languages on their own instead of being "taught" the languages by bilingual human programmers -- has taken off.
That advantage comes from the firm's background in translating by computer (known as "machine translation" in the argot).
Natural language processing (NLP), which is the "understanding" of the natural human language by computers, involves machine translation, information retrieval, and question answering.
Because of the limits of most computer keyboards and machine translation sites, it has been difficult, if not impossible, for non-English speakers who do not use the Roman alphabet to compose e-mail notes or post messages on a Web site in their own languages.
Our model builds on state of the art progress in computer vision and machine translation: we extract car region proposals with Region Convolutional Neural Networks R-CNN) aNetworks R-CNNinto fixed-sized vectors.
Experts divide translation technology in two distinct categories: machine translation (MT) which relies solely on software, and computer-assisted translation (CAT) which is simply used as an aid for translators.
