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Total results: 1731  Total pages: 174  This page: 1

Post Editing | Katie Botkin
Multilingual Jul/Aug 2016

Once upon a time, there was a computer. And the computer grew gradually smarter and smarter, fed by bites of data, slowly at first, and then faster as it learned how to feed itself. Soon it could process more data than any human, could store more information than any human brain....

Perspectives: Working together on open source | Marc Mittag
Multilingual Jul/Aug 2016

Translation processes have become technically more complex since the beginning of the 1990s, when translation memories (TMs) entered the industry. The development has increased rapidly in recent years and will continue to do so in the future. This is one reason why prices have been under pressure ever since.

But there is another current trend related to this....

Community Lives: Localization volunteer communities | Jeannette Stewart
Multilingual Jul/Aug 2016

The volunteer communities within the language industry fall under three distinct categories: developer communities, translator communities and end-user communities. Volunteer engineers donate their time and coding skills to community open source projects such as Okapi, Apertium, OmegaT and many more. Translator volunteers donate their time and translation skills to mostly humanitarian causes such as crisis relief initiatives through Translators without Borders or inspirational community reach through TED Talks, just to mention a couple. End-user communities are actual application users, as opposed to those who design and build them, contributing by localizing the application for their locale, examples of which are Mozilla, Facebook and Wikipedia....

Perspectives: Artificial intelligence and the creative translator | Anja Jones
Multilingual Jul/Aug 2016

There is a lot of debate about ethics in AI and how legal systems would cope with truly intelligent machines. And quite understandably, speculation is rife about which jobs run the most risk of being replaced by machines in the long run. Take Japan, for example, where carebots (AI robots) are already taking care of patients, replacing human nurses. In an age where everything that can be automated will be automated in the name of progress, is the job of the translator next on the list? And if we really are, as some scientists and futurologists suggest, on the verge of “singularity” (the moment in time where artificial intelligence will surpass human intelligence), would it be conceivable to create a machine that can translate creatively?...

Machine translation with brains | Dimitar Shterionov
Multilingual Jul/Aug 2016

When humans translate a given text, they focus on translating small chunks of text, or phrases, one after the other and then link them together to form a complete translation of the source text. Choosing the most appropriate translation of a given phrase is based on the meaning of the phrase itself and the meaning of one or more of the immediately preceding phrases, as well as the context. But the specifics of the target language also need to be considered in order for a translation to be both adequate and fluent....

Bringing APIs to the translation industry | Diego Bartolome
Multilingual Jul/Aug 2016

Every time I hear about how the translation industry is evolving into a technology sector, I think about APIs. In simple terms, APIs allow your software to connect to other IT systems, and be connected to third party applications.

How many translation companies in our market have a clear and defined strategy for APIs?...

Emerging technologies: Innovations and disruptions | Hélène Pielmeier
Multilingual Jul/Aug 2016

Deep learning technologies will eventually deconstruct linguistic and semantic components, leveraging that computing power and neural network to improve the quality and usability of the translated content. For example, Microsoft recently released neural net-based translation apps with offline capability for both iOS and Android. While their quality is poor, the fact that they can run at all on limited hardware is an impressive achievement....

Origins and functionalities of Microsoft’s MAT tool | Serge Gladkoff
Multilingual Jul/Aug 2016

Many localization companies are aware of sophisticated software translation tools that are being used for translation of software strings in large software products. But what about simpler software? How can small software developers without complex processes jumpstart software translation to see their products become multilingual for global reach?...

Errors in MT and human translation | Silvio Picinini
Multilingual Jul/Aug 2016

There are several errors that result from the use of TM. These memories offer the human translator suggestions of translation that are similar to the segment they are translating. Similar does not mean equal, so if the suggested translation is a fuzzy match, the human translator must make changes. If they don’t make any change and accept the fuzzy match as it is, they risk making errors. There are three subtypes of errors to mention here:...

Translating Rapanui | Daria Kizilova
Multilingual Jul/Aug 2016

Rapanui is considered one of the most diverse languages within Austronesian linguistics. Spoken by around 2,700 people on Easter Island, called Rapa Nui by natives, the language has been conditioned by its ambiguous history and multicultural influences during the thousands of years of its existence. Due to these facts, it is almost impossible to determine the process of adhering new words and linguistic constructions that have influenced this Eastern Polynesian language since it was born....


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Total results: 1731  Total pages: 174  This page: 1