Rosetta Stone Buys Up Online Language Learning Community Livemocha For $8.5M In Cash

Originally posted on TechCrunch:

Rosetta Stonehas just acquired Seattle-based online language-learning community Livemocha for $8.5 million in cash. Livemocha has one of the largest online language-learning communities in the world, boasting over 16 million members from over 195 different countries.

Rosetta Stone will likely use Livemocha’s cloud platform to offer its language packages online — they were once only available via disc set, with a complimentary iPad app.

Here’s what Rosetta Stone CEO and President Steve Swad had to say about it:

We are in the process of transforming Rosetta Stone to be the most dynamic and ubiquitous technology-based learning platform in the world. Our acquisition of Livemocha will help accelerate that transformation. With Livemocha and its vibrant online community on our side, Rosetta Stone will reach more people and change more lives than ever before.

Livemocha will remain in its Seattle-based offices, adding yet another arm to Rosetta Stone’s US presence which…

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Machine Translation in Short

Originally posted on Pangeanic Translation Technologies & News:

It is evident that certain documents require a human translator in order to interpret the subtleties of a language. Nevertheless, no matter how skilled a human translator may be, machine translation (also known as automatic translation or MT for short) exceeds the efficiency of a human translator.

Machine translation is generally used for subject-specific cases and this is where results and productivity rates are spectacularly higher. It allows individuals and companies to tailor their work according to the topic. Consequently, this enriches the output and quality of machine translation by cutting down on the number of choices for each word(s) to be translated.

This form of translation is extremely helpful in areas where formal language is used or phrases are repeated without much variation, such as administrative documents, which do not require the use of colloquial language and expression.

The potential of machine translation has been increasingly explored. In 2009…

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Interview with Marja Vaba – Translation Quality Manager

Marja Vaba is a Translation Quality Manager working for one of the biggest and most innovative IT world companies. Her contribution is very useful to understand in which direction the translation/localization is going towards terminology management.

1) Linda Burchi: Could you please introduce yourself? What is your academic background, work experience and current position?
Marja Vaba: I have worked as Translation Quality Manager in a quickly growing software company for almost five years. When I started, it still felt like working in a start-up where going with the ideas was more important and beneficial than following documentation. Now the company is so big that following processes and having decent documentation and data-bases is essential for quality assurance in both software development and translation (quality) management. I have had the chance to build up the translation quality system in the company. I have found my background in linguistics and translation industry useful for that.

2) Linda Burchi: Is terminology an important variable on the web?
Marja Vaba: I think that terminology is an important variable in all texts because the consistent and systematic way we call things and phenomena is very important for reader/user understanding. There is also another aspect: in web, new products emerge very often, thus well-coined and motivated terms help users to understand better what they are buying/dealing with.

3) Linda Burchi: Has the web communication an influence on terminology?
Marja Vaba: It certainly does. In web, everybody has access not only to information, but also freedom to express their opinion. This also counts for terminology – kind of crowdsourcing is going on all the time if a company listens to its users/community.

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From Crude Rules for Classification to Machine Learning

Originally posted on Aiaioo Labs Blog:

I recently gave a lecture at a college in Bangalore where I explained to a roomful of undergrads how machine learning algorithms can be constructed incrementally starting from crude rule-based algorithms.  I explained how you could start with a list of words and make incremental improvements till you obtained a Naive Bayesian Classifier.

Here are the slides on the transition from crude rule based methods to machine learning that I presented at the college (showing how the incremental transition can be made).

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