Machine learning is accomplishing Facebook's mission of connecting the environment throughout language obstacles. Fb is now serving 2 billion textual content translations for every working day. Fb can translate throughout 40 different languages in 1800 instructions like French to English. And 800 million people, pretty much 50 % of all Fb people, see translations just about every month. Alan Packer, Facebook's Director of Engineering for language technological innovation, discovered this progress nowadays at MIT's Emtech Electronic convention in San Francisco.

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.

Need a translation app for your vacation but don't want to get slammed with a big data bill? New updates to Google Translate, including the addition of another major language and a pop-up translation feature, can help. Google said today that its translation app -- which can translate in a number of ways including hearing someone speak or by reading what they write -- can now be used in offline mode with no data or Wi-Fi connection on both iOS and Android (it previously wasn't available on iOS), potentially eliminating the high price of data for travelers with iPhones. Some 52 languages, including French, German, Russian and, most recently, Filipino, can be translated offline. A complete list is here.

If trying to order dinner or find your hotel abroad fills you with fear due to your abysmal grasp of foreign languages, don't panic. A forthcoming in-ear gadget is claimed to be able to translate speech like the Babel Fish in The Hitchhiker's Guide to the Galaxy, or the Universal Translator gadget in Star Trek. The system, dubbed the Pilot, comprises two earpieces to be worn by two people who do not speak the same language and uses an app so the duo can converse with ease. The Pilot system comprises two earpieces (shown in three colours above) to be worn by two people who don't speak the same language It is claimed to be the first'smart earpiece' capable of translating between two languages. The company behind the technology, Waverly Labs, said: 'This little wearable uses translation technology to allow two people to speak different languages but still clearly understand each other.'

Recent advances in conditional recurrent language modelling have mainly focused on network architectures (e.g., attention mechanism), learning algorithms (e.g., scheduled sampling and sequence-level training) and novel applications (e.g., image/video description generation, speech recognition, etc.) On the other hand, we notice that decoding algorithms/strategies have not been investigated as much, and it has become standard to use greedy or beam search. In this paper, we propose a novel decoding strategy motivated by an earlier observation that nonlinear hidden layers of a deep neural network stretch the data manifold. The proposed strategy is embarrassingly parallelizable without any communication overhead, while improving an existing decoding algorithm. We extensively evaluate it with attention-based neural machine translation on the task of En->Cz translation.

Translating text on your smartphone just got a whole lot easier. Google on Wednesday rolled out new updates for the Android and iOS versions of its Translate app. Android users are getting a new feature called Tap to Translate, which lets you translate text just by highlighting it. The feature works in any app that lets you highlight, according to Google. When you select some text, a Translate icon will appear at the top of your screen.

Translate for iOS now includes offline support, giving you a way to communicate in other languages when you don't have data service (say, on vacation). And if you regularly visit China, you'll be glad to know that camera-based Word Lens translation on both Android and iOS now supports simplified and traditional Chinese. If you've ever struggled to make sense of a Beijing restaurant menu or a Shanghai street sign, you can rest easy.

Please make Pull Requests for good resources, or create Issues for any feedback! Seq2Seq solves the traditional fixed-size input problem thatEffective Approaches to Attention-based Neural Machine Translation prevents traditional DNNs from mastering sequence based tasks such as translation and question answering. It has been shown to have state of the art performances in English-French and English-German translations and in responding to short questions. Seq2Seq was first introduced in late 2014 by 2 papers (Sequence to Sequence Learning with Neural Networks and Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation) from Google Brain and Yoshua Bengio's group. The two papers took a similar approach in machine translation, in which Seq2Seq was developed upon.

Machine translation (MT) systems such as Google Translate have become part of our daily life. But how do they work? In this talk, I'll explain how these systems are built. In the first part of my talk, I'll present a general overview of the field and the key ideas driving modern MT systems. In the second part, I'll dig deeper into the statistical techniques used to estimate translation models from data, and discuss some of the current hot topics in the field."

Microsoft's Translator app for Android now has the ability to translate text within images. Its latest update also adds additional downloadable language packs, and an inline translator that makes understanding foreign text in other apps even easier. With the ability to translate text within images, Translator no longer requires you to enter words and phrases manually. You can take a picture of signs, menus, leaflets, emails, and more -- and Translator will find the text within them, then translate it into your native tongue. What's more, Translator supports real-time image translation, which means you don't have to snap a picture first and then translate it; simply point your camera at the subject and it will work its magic instantly.