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.

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.