In this paper we propose an approach to modeling syntactically-motivated skeletal structure of source sentence for machine translation. This model allows for application of high-level syntactic transfer rules and low-level non-syntactic rules. It thus involves fully syntactic, non-syntactic, and partially syntactic derivations via a single grammar and decoding paradigm. On large-scale Chinese-English and English-Chinese translation tasks, we obtain an average improvement of +0.9 BLEU across the newswire and web genres.

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-ofthe-art NMT system on Chinese-to-English translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.

The marriage of computing power and data is finally bearing fruit in the field of cognitive computing, sometimes called machine learning or, more controversially, artificial intelligence. In its most everyday form, we see it in tools such as Google Translate or Microsoft's Bing Translate, which can translate phrases and documents effortlessly across multiple languages. More futuristically, the promise of self-driving vehicles, which can complete entire road journeys without driver intervention, is already being realised. Yet the biggest revolution in work is happening at some of the most basic levels, such as reading and dissecting legal documents to extract meaning and useful information. The tedious slog of work can be transformed by computers which are able to read and parse legal phrases, and summarise them or enter relevant details into a database or spreadsheet.

When Microsoft launched the offline functionality for Android, it was really bringing the experience in line with Google's offering on the platform. But while the search giant's Translate app for Android does offline translation of text (and even photos containing text), its iOS app is online-only. That makes Microsoft's Translate app the first from a major company to offer the functionality, and the first ever on the platform to use a neural network to achieve it. The iOS app supports 43 languages, although you'll have to download the relevant libraries before going offline. That's a lot more than the nine the Android version launched with, but Microsoft says it's updating that app to support the expanded catalog.

Microsoft today announced that its Microsoft Translator app for iOS devices can now translate text and images from one language to another even when you're offline. The app already supported this functionality on Android, and the competing Google Translate for Android could work offline, too. But in this case, Microsoft has beat Google to the punch -- Google Translate currently works offline only on Android. "Until now, iPhone users needed an Internet connection if they wanted to translate on their mobile devices. Now, by downloading the Microsoft Translator app and the needed offline language packs, iOS users can get near online-quality translations even when they are not connected to the Internet. This means no expensive roaming charges or not being able to communicate when a data connection is spotty or unavailable," the Microsoft Translator team wrote in a blog post.

Microsoft today announced that its Microsoft Translator app for iOS devices can now translate text and images from one language to another even when you're offline. The app already supported this functionality on Android, and the competing Google Translate for Android could work offline, too. But in this case, Microsoft has beat Google to the punch -- Google Translate currently works offline only on Android. "Until now, iPhone users needed an Internet connection if they wanted to translate on their mobile devices. Now, by downloading the Microsoft Translator app and the needed offline language packs, iOS users can get near online-quality translations even when they are not connected to the Internet. This means no expensive roaming charges or not being able to communicate when a data connection is spotty or unavailable," the Microsoft Translator team wrote in a blog post.

Artificial intelligence is being used all around is, but it looks nothing like The Jetsons. So why are people panicked that robots will take their jobs? The World Economic Forum warned that robots and technological advances will take more than 5 million jobs from humans over the next five years. Machine learning has undoubtedly earned its place in the workforce, but machines don't necessarily have to replace humans – they can in fact enhance the work humans can do. One area where machine learning is flourishing is in the localisation and translation industry.

Google confirmed they plan to improve Google Translate's accuracy through artificial intelligence called deep learning. Deep learning is an advanced model of machine learning where an algorithm takes what it has already "learned" (data previously processed) and uses it to form new ways to solve problems in a pattern. Jeff Dean, Google Senior Fellow, confirmed that his team has been working with the Google Translate team to "scale out experiments with translation based on deep learning." Deep learning and technologies derived from it, including deep and recurrent neural nets, are objectively excellent at tackling sequential problems such as speech and image recognition, as long as there is sufficient existing material to train them. On that front, Google Translate's vast data trove of translated material should indeed prove quite useful.

Artificial intelligence is on the rise in the world of machine translation. A string of recent news about tech giants bolstering machine translation engines with deep learning underscores just how central integrating deep learning into machine translation products has become for companies like Google and Microsoft. Slator reached out to a representative of Beijing-based Baidu, who is authorized to speak for the company, to get an exclusive look at what the Chinese tech leader has in store for its translation technology. Baidu began R&D on Baidu Translate in 2010, launching the product in June 2011. The company felt that translation was in line with what their search users needed.

Microsoft Translator is a cloud-based automatic text and speech translation (a.k.a.