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Automated Translation (MT) Trends
Material related to the use and continued development of machine translation
Curated by K Vashee
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Microsoft Skype Translator Preview: The real-world test

Microsoft Skype Translator Preview: The real-world test | Automated Translation (MT) Trends | Scoop.it
Our conversation was halting at first. Maria in the south of Spain would start to speak and I would reflexively respond, even though I didn’t understand what she was saying. Our...
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A picture is worth a thousand (coherent) words: building a natural description of images

A picture is worth a thousand (coherent) words: building a natural description of images | Automated Translation (MT) Trends | Scoop.it

Via Dr. Stefan Gruenwald
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Natural Language Careers's curator insight, November 19, 8:53 AM

Google making progress towards automatic captioning.  Cool stuff.

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What do job trends in the translation industry mean to you?

What do job trends in the translation industry mean to you? | Automated Translation (MT) Trends | Scoop.it
Jobs in the translation industry related to automated translation point to a future of greater automation and faster multilingual content generation.
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Stanford system combines software with human intelligence to improve translation

Stanford system combines software with human intelligence to improve translation | Automated Translation (MT) Trends | Scoop.it
Using software to suggest word choices makes professional translators more productive in the $34-billion-a-year market for foreign language translation.
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eMpTy Pages: Understanding The Drivers of Success with the Business Use of Machine Translation

eMpTy Pages: Understanding The Drivers of Success with the Business Use of Machine Translation | Automated Translation (MT) Trends | Scoop.it
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The Incredible Shrinking Planet

The Incredible Shrinking Planet | Automated Translation (MT) Trends | Scoop.it
What happens when we bridge the geographic and linguistic gaps that have separated us for centuries?
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Essays in English yield information about other languages

Essays in English yield information about other languages | Automated Translation (MT) Trends | Scoop.it

Cambridge, Massachusetts - Computer scientists at MIT and Israel’s Technion have discovered an unexpected source of information about the world’s languages: the habits of native speakers of those languages when writing in English.

The work could enable computers chewing through relatively accessible documents to approximate data that might take trained linguists months in the field to collect. But that data could in turn lead to better computational tools.

“These [linguistic] features that our system is learning are of course, on one hand, of nice theoretical interest for linguists,” says Boris Katz, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory and one of the leaders of the new work. “But on the other, they’re beginning to be used more and more often in applications. Everybody’s very interested in building computational tools for world languages, but in order to build them, you need these features. So we may be able to do much more than just learn linguistic features. … These features could be extremely valuable for creating better parsers, better speech-recognizers, better natural-language translators, and so forth.”

In fact, Katz explains, the researchers’ theoretical discovery resulted from their work on a practical application: About a year ago, Katz proposed to one of his students, Yevgeni Berzak, that he try to write an algorithm that could automatically determine the native language of someone writing in English. The hope was to develop grammar-correcting software that could be tailored to a user’s specific linguistic background.

Family resemblance

With help from Katz and from Roi Reichart, an engineering professor at the Technion who was a postdoc at MIT, Berzak built a system that combed through more than 1,000 English-language essays written by native speakers of 14 different languages. First, it analyzed the parts of speech of the words in every sentence of every essay and the relationships between them. Then it looked for patterns in those relationships that correlated with the writers’ native languages.

Like most machine-learning classification algorithms, Berzak’s assigned probabilities to its inferences. It might conclude, for instance, that a particular essay had a 51 percent chance of having been written by a native Russian speaker, a 33 percent chance of having been written by a native Polish speaker, and only a 16 percent chance of having been written by a native Japanese speaker.


Via Charles Tiayon
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Interesting way to build new ways to link two languages together

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Charles Tiayon's curator insight, July 27, 8:37 PM

Cambridge, Massachusetts - Computer scientists at MIT and Israel’s Technion have discovered an unexpected source of information about the world’s languages: the habits of native speakers of those languages when writing in English.

The work could enable computers chewing through relatively accessible documents to approximate data that might take trained linguists months in the field to collect. But that data could in turn lead to better computational tools.

“These [linguistic] features that our system is learning are of course, on one hand, of nice theoretical interest for linguists,” says Boris Katz, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory and one of the leaders of the new work. “But on the other, they’re beginning to be used more and more often in applications. Everybody’s very interested in building computational tools for world languages, but in order to build them, you need these features. So we may be able to do much more than just learn linguistic features. … These features could be extremely valuable for creating better parsers, better speech-recognizers, better natural-language translators, and so forth.”

In fact, Katz explains, the researchers’ theoretical discovery resulted from their work on a practical application: About a year ago, Katz proposed to one of his students, Yevgeni Berzak, that he try to write an algorithm that could automatically determine the native language of someone writing in English. The hope was to develop grammar-correcting software that could be tailored to a user’s specific linguistic background.

Family resemblance

With help from Katz and from Roi Reichart, an engineering professor at the Technion who was a postdoc at MIT, Berzak built a system that combed through more than 1,000 English-language essays written by native speakers of 14 different languages. First, it analyzed the parts of speech of the words in every sentence of every essay and the relationships between them. Then it looked for patterns in those relationships that correlated with the writers’ native languages.

Like most machine-learning classification algorithms, Berzak’s assigned probabilities to its inferences. It might conclude, for instance, that a particular essay had a 51 percent chance of having been written by a native Russian speaker, a 33 percent chance of having been written by a native Polish speaker, and only a 16 percent chance of having been written by a native Japanese speaker.

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Prof. Philipp Koehn - Open Problems in Machine Translation - YouTube

Professor Philipp Koehn: Personal Chair in Machine Translation delivered his inaugural lecture entitled "Open Problems in Machine Translation". Recorded on T... ([YouTube] Prof.
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Great overview of challenges for MT

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Serge AWONO's curator insight, October 8, 2:47 AM

Professor Philipp Koehn: Personal Chair in Machine Translation delivered his inaugural lecture entitled "Open Problems in Machine Translation

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What will the Internet look like in 100 years?

What will the Internet look like in 100 years? | Automated Translation (MT) Trends | Scoop.it
The Internet is a strange and wonderful place, full of magic and mystery and videos of kittens doing cute kitten things. The digital world evolves so quickly, though — can you even imagine what the...

Via Isabella Massardo
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Siri Features And Improvements

Siri Features And Improvements | Automated Translation (MT) Trends | Scoop.it
Microsoft's Vikram Dendi, a technical and strategy advisor for Microsoft Research, said the company would be "far away from getting this to the user" if it wasn't for neural networks in a previous interview with Business Insider. So what does this mean for Siri? If Wired's report turns ...
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How do Computers Learn a New Language? -- An Introduction to Statistical Machine Translation - YouTube

This META-NET video takes a look at how Statistical Machine Translation works in practice. The basic concepts and principles are explained in a high-level wa...
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What’s so awful about machine translation? - Linguagloss

What’s so awful about machine translation? - Linguagloss | Automated Translation (MT) Trends | Scoop.it
My first introduction to machine translation was truly …
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Stanford system combines software with human intelligence to improve translation

Stanford system combines software with human intelligence to improve translation | Automated Translation (MT) Trends | Scoop.it
Using software to suggest word choices makes professional translators more productive in the $34-billion-a-year market for foreign language translation.
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Google Translate Sings: "Wrecking Ball" by Miley Cyrus (PARODY) - YouTube

#ballinthesink And now for something COMPLETELY different, a clothed Google Translate of Wrecking Ball!! Enjoy the silliness!! SUBSCRIBE and be sure to like ...
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Neural networks draw on context to improve machine translations

Neural networks draw on context to improve machine translations | Automated Translation (MT) Trends | Scoop.it
Dutch researchers have improved the output of a statistical machine translation system by examining the context in which words are found
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UTIC-2014. Understanding MT ROI & Best Practices — Presentation Videos — Ukrainian Translation Industry Conference

UTIC-2014. Understanding MT ROI & Best Practices — Presentation Videos — Ukrainian Translation Industry Conference | Automated Translation (MT) Trends | Scoop.it
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Translation and Technology: Where are we heading?

Translation and Technology: Where are we heading? | Automated Translation (MT) Trends | Scoop.it
The effects of new digital means of translation are starting to affect the way we interact with the world. If everyone can understand every foreign language with the use of new technology, how can ...
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How are machine translation engines trained? A look under the hood.

How are machine translation engines trained? A look under the hood. | Automated Translation (MT) Trends | Scoop.it
How are machine translation engines trained to run like the well-oiled machines? Learn how they continue to produce better quality translations over time.
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The revolution of machine translation by Gabriel Guzovsky (CAT Tools,Technology) - ProZ.com translation articles

The revolution of machine translation by Gabriel Guzovsky (CAT Tools,Technology) - ProZ.com translation articles | Automated Translation (MT) Trends | Scoop.it
Translation article entitled "The revolution of machine translation" ("The revolution of machine translation" - http://t.co/j7m44OETEb by G.Guzovsky)...
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A translator who does not see MT as the horrific enemy but PT is probably one of the best MT languages for Google

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Are Machines Creating a Universal Language? - iQ by Intel

Are Machines Creating a Universal Language? - iQ by Intel | Automated Translation (MT) Trends | Scoop.it
Language unites and separates us. It allows us to express our ideas and shared cultural values, but it can also fence us in when we encounter an unfamiliar
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eMpTy Pages: The Expanding Translation Market Driven by Expert Based MT

eMpTy Pages: The Expanding Translation Market Driven by Expert Based MT | Automated Translation (MT) Trends | Scoop.it
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Interesting discussion in comments

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▶ 22 Hans Uszkoreit (DFKI): Quality Machine Translation for the 21st Century (MLW 7) - YouTube

Over the last 10 years Machine Translation (MT) has started to make substantial in-roads in professional translation (localisation and globalisation) and our...
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Jill Lepore: What the Theory of “Disruptive Innovation” Gets Wrong

Jill Lepore: What the Theory of “Disruptive Innovation” Gets Wrong | Automated Translation (MT) Trends | Scoop.it
Clayton Christensen’s theory of “disruptive innovation” is founded on anxiety, fear, and shaky evidence.

Via Isabella Massardo
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Isabella Massardo's curator insight, June 16, 3:37 AM

Replacing “progress” with “innovation” skirts the question of whether a novelty is an improvement: the world may not be getting better and better but our devices are getting newer and newer.