More than one billion people use Facebook every day. If you picked two at random, they most likely wouldn't speak each other's language. But the company thinks they should still be able to socialize with each other. A new feature uses automatic translation software to help people post Facebook updates in multiple languages at the same time. Users viewing a post made that way are shown the version most likely to be readable to them in light of their own past language use and settings.

More than 1.5 billion people use Facebook. And only half speak English. The rest speak so many dozens of other languages, effectively silo'd off from the English speakers and, in many cases, from each other. If you stumble onto a Facebook post in a foreign language, Facebook lets you instantly translate it--in a semi-effective way. And beginning today, millions of people will have the option of instantly translating their own posts into any one of 44 other languages, so that they will automatically show up in your News Feed in your native tongue.

This is how it works. When you're writing a post, you'll see some text asking you if you want the post to appear in another language. Click it, and you can then choose which languages you want from a drop-down list. It'll then automatically fill out separate messages with the appropriate machine-translated text -- the sort that you'd find on Google Translate, for example. You can go with these if you like, but if you're multilingual and that machine-translation isn't up to snuff, you can actually go in and fix it up so it reads correctly in those other languages. Doing this also helps teach Facebook's machine-translation to get better over time.

I have been invited to write a book chapter on lexical choice for translators (contact me if you want to see a preprint). To get acquainted on this audience different from my usual computer science I read a few papers on professional translators use of technology. Two of them are quite interesting and I recommend them not only because they make for a good read and they have implications outside translation: Translation Skill-sets in a Machine-translation Age by Anthony Pym (2013) and Is Machine Translation Post-editing Worth the Effort?: A Survey of Research into Post-editing and Effort by Maarit Koponen (2016). This search finished by reading a short ebook by researchers at the MIT Center for Digital Business titled Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. In that book plus the papers there's this call for humans, if we want to remain employed, to hybridize our work and to seek out ways to work with the computer as some sort of partnership.

Neural Machine Translation (MT) has reached state-of-the-art results. However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and morphologically rich languages. In this paper, we propose a neural MT system using character-based embeddings in combination with convolutional and highway layers to replace the standard lookup-based word representations. The resulting unlimited-vocabulary and affix-aware source word embeddings are tested in a state-of-the-art neural MT based on an attention-based bidirectional recurrent neural network. The proposed MT scheme provides improved results even when the source language is not morphologically rich. Improvements up to 3 BLEU points are obtained in the German-English WMT task.

Imagine a far flung land where you can catch a ride from the Jackie Chan bus stop to a restaurant called Translate Server Error, and enjoy a hearty feast of children sandwiches and wife cake all washed down with some evil water. If such a rich lunch gets stuck in your gnashers, you'll be pleased to know there are plenty of Methodists on hand to remove your teeth. And if by this point you've had enough of the bus, fly home in style on a wide-boiled aircraft. But whatever you do, please remember that when you land at the airport, eating the carpet is strictly prohibited. No, I haven't gone mad.

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.