Combining Machine Translation (MT) with auto-adaptive Machine Learning (ML) enables a new paradigm of machine assistance. Such systems learn from the experience, intelligence and insights of their human users, improving productivity by working in partnership, making suggestions and improving accuracy over time. The net result is that human reviewers produce far higher volumes of content, with nearly the same level of quality, for a fraction of the time and cost. Machine assistance can save customers up to one half (or more) of the price of traditional high-quality human translation services. Or, if you've been used to machine translation alone and have been unhappy with the results, watch your translation quality rise dramatically with a marginal increase in price.

Microsoft's annual developer conference, //build/, was held March 30th to April 1st in San Francisco. During the conference, we unveiled a new version of Microsoft Translator API that adds real-time speech translation capabilities to the existing text translation API. Powered by Microsoft's state-of-the-art artificial intelligence technologies, speech translation has been available in Skype or over…

How do I format these testsets to a one-segment-per-line format?

"About 6,000 languages are currently spoken in the world today," says Elizabeth Salesky of MIT Lincoln Laboratory's Human Language Technology (HLT) Group. "Within the law enforcement community, there are not enough multilingual analysts who possess the necessary level of proficiency to understand and analyze content across these languages," she continues. This problem of too many languages and too few specialized analysts is one Salesky and her colleagues are now working to solve for law enforcement agencies, but their work has potential application for the Department of Defense and Intelligence Community. The research team is taking advantage of major advances in language recognition, speaker recognition, speech recognition, machine translation, and information retrieval to automate language processing tasks so that the limited number of linguists available for analyzing text and spoken foreign languages can be used more efficiently. "With HLT, an equivalent of 20 times more foreign language analysts are at your disposal," says Salesky.

Machine learning is accomplishing Facebook's mission of connecting the world across language barriers. Facebook is now serving 2 billion text translations per day. Facebook can translate across 40 languages in 1,800 directions, like French to English. And 800 million users, almost half of all Facebook users, see translations each month. That's all based on Facebook's own machine learning translation system.

Professor Mirella Lapata has received five years' funding for her project, TransModal: Translating from Multiple Modalities into Text. The European Research Council (ERC) Consolidator Grant worth 1.9M will begin in September. ERC Consolidator Grants are for researchers of any nationality with 7-12 years of experience since completion of their PhD (plus 18 months for each child), a scientific track record showing scientific talent and an excellent research proposal. Professor Lapata's award winning proposal is summarised on the ERC website as follows: "Recent years have witnessed the development of a wide range of computational methods and tools that process and generate natural language text. Many of these have become familiar to mainstream computer users such as tools that retrieve documents matching a query, perform sentiment analysis, and translate between languages. Indeed, publicly available systems like Google Translate can instantly translate between any pair of over fifty human languages allowing users to access web content that wouldn't have otherwise been available. "The accessibility of the web could be further enhanced with applications that not only translate between different languages (eg.

Machine learning is accomplishing Facebook's mission of connecting the world across language barriers. Facebook is now serving 2 billion text translations per day. Facebook can translate across 40 languages in 1,800 directions, like French to English. And 800 million users, almost half of all Facebook users, see translations each month. That's all based on Facebook's own machine learning translation system.

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.