Most studies find that neural machine-translation models can translate only about 30 percent of novel excerpts—usually simple passages—with acceptable quality, as determined by native speakers.

Now, thanks to advances in artificial intelligence and machine translation, these barriers are being broken down. Written by Eileen Brown, Contributor Nov. 24, 2016, 5:49 a.m. PT ...

Fortunately, neural networks eat big, complicated data sets for breakfast. Google has been working on a machine learning translation technique for years, and today is its official debut.

Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.

On the WMT’14 English-to-French and English-to-German benchmarks, Google’s Neural Machine Translation achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set ...

Smartling, the enterprise translation solutions company, today announced a major product expansion for its Neural Machine Translation (NMT) Hub, makin ...

The Google Neural Machine Translation system 'surpasses' the results of all other machine-translation solutions currently available, with GNMT now being used for Chinese-to-English translations.

Google Translate, for example, is getting a technical makeover with the introduction of Neural Machine Translation (NMT). Starting today, you’ll notice vast improvements for any translations ...

A project will apply neural machine translation to Spanish, Portuguese, Catalan, Galician, Asturian, Aragonese and Aranese Universitat Oberta de Catalunya (UOC) ...

So now, Google is “serving 35 percent of all translation requests using neural networks,” Turovsky said. Eventually, he said, Google will use the system for all 103 languages.