Researchers at the University of Amsterdam are using neural networks to help a statistical machine translation systems learn what all human translators know--that the best translation of a word often depends on the context. Such tools are increasingly important as individuals and businesses seek to access information or buy products and services from other countries where different languages are spoken. Statistical machine translation work by breaking sentences into phrase fragments and selecting the most likely translation for each fragment--a process that doesn't always yield the best translation for the sentence as a whole in morphologically rich languages such as those where nouns are inflected for number, case and gender. To improve the word selection of such systems when translating into morphologically rich languages such as Russian, Bulgarian and German, the team used a neural network to analyze the words in context in the source language. Translating sentences into grammatically more complex languages is relatively easy for human translators because they understand the grammatical function of the word in a sentence.

Facebook is acquiring a company that specializes in speech interpretation and translation software. The move, disclosed Monday, could help Facebook better connect its users across the globe. The deal to acquire Mobile Technologies was announced in a blog post by Facebook product management director Tom Stocky. Terms of the acquisition were not disclosed. "We believe this acquisition is an investment in our long-term product roadmap," he said.

In 2001, Google started providing a service that could translate eight languages to and from English. It used what was then state-of-the-art commercial machine translation (MT), but the translation quality wasn't very good, and it didn't improve much in those first few years. In 2003, a few Google engineers decided to ramp up the translation quality and tackle more languages. That's when I got involved. I was working as a researcher on DARPA projects looking at a new approach to machine translation--learning from data--which held the promise of much better translation quality.

Translation software that develops an understanding of languages by scanning through thousands of previously translated documents has been released by US researchers. Most existing translation software uses hand-coded rules for transposing words and phrases. But the new software, developed by Kevin Knight and Daniel Marcu at the Information Sciences Institute, part of the University of Southern California, US, takes a statistical approach, building probabilistic rules about words, phrases and syntactic structures. The pair founded a company called Language Weaver in Los Angeles, US, to sell the software as an automated translation tool. They already offer technology that can translate to or from English with four languages – Arabic, Chinese, French and Spanish.

Technology has brought us all closer together, and thanks to the Internet, we can communicate with loved ones on a daily basis, even if they are across the world. In an effort to bring people even closer and break language barriers Google announced that its Gmail service will soon include an "automatic translation" feature for all users. "The next time you receive a message in a language other than your own, just click on Translate message in the header at the top of the message," wrote the company in a blog post. "It will be instantly translated into your language." The feature will roll out over the next few days.

HURSLEY, UK--(Marketwire - September 13, 2007) - IBM (NYSE: IBM) has developed an ingenious system called SiSi (Say It Sign It) that automatically converts the spoken word into British Sign Language (BSL) which is then signed by an animated digital character or avatar. SiSi brings together a number of computer technologies. A speech recognition module converts the spoken word into text, which SiSi then interprets into gestures, that are used to animate an avatar which signs in BSL. Upon development this system would see a signing avatar'pop up' in the corner of the display screen in use -- whether that be a laptop, personal computer, TV, meeting-room display or auditorium screen. Users would be able select the size and appearance of the avatar.

Thanks to some clever technology, which Sonico suggests is "sophisticated voice recognition and machine translation software," users can communicate clearly and precisely by simply talking into their iPhone, iPad or iPod touch. Interestingly, it's revealed in the credits to be Nuance and Microsoft Translator powering the app. A simple interface, rather bare on the iPad, shows two button shaped microphones, each with the flag of the country you want to translate from and too. Tap to speak, tap the button again when you stop speaking, and both a written version of your speech and translation quickly appears, along with an audio translation, complete with convincing accent. It's all very simple, mostly accurate and greatly impressive, not least as iTranslate Voice can currently be had for as little as $0.99, an absolute bargain.

The MT evaluation series started in 2001 as part of the DARPA TIDES program. In their current form, the evaluations are driven and coordinated by NIST as NIST OpenMT. They provide an important contribution to the direction of research efforts and the calibration of technical capabilities in MT. The OpenMT evaluations are intended to be of interest to all researchers working on the general problem of automatic translation between human languages. To this end, they are designed to be simple, to focus on core technology issues, to be fully supported, and to be accessible to all those wishing to participate.

Google Translate is currently best known for being a quick and dirty way to render Web pages or short text snippets in another language. But according to Der Spiegel, the next step for the core technology behind that service is a device that amounts to the universal translator from "Star Trek." Apparently everyone from Facebook to Microsoft is ramping up similar ambitions: to create services that eradicate language barriers as we currently know them. Machine translation has been around in one form or another for decades, but has always lagged far behind translations produced by human hands. Much of the software written to perform machine translation involved defining different languages' grammars and dictionaries, a difficult and inflexible process.

In addition to teaching the engine a new language, they also involved members of the community, partners and collaborators to create and review improved versions of the automated translation system, and collect qualitative feedback about each "trained" system. Deploying a system that reaches a certain level of quality allows seamless use with the standard Microsoft Translator APIs, and many scenarios powered by the API, like the web translation widget. Feedback that is generated through these scenarios can be utilized again in the training process – creating a virtuous loop for improving the translation quality.