Machine Translation

Stay on top of the latest trends in Machine Translation. 

Make Machine Translation Part of Your Content Journey

Identify which content is appropriate for Machine Translation and learn why you should adopt the technology.

How would you like to translate more content, do it faster and keep costs contained to your current budget? Sound too good to be true? It’s not. Machine Translation (MT) can help you augment your translation productivity without increasing costs.

MT is not for all content. But the technology’s usefulness is growing due to recent improvements in quality. Embracing MT will enable you to keep up with global organizations that have unleashed the power of MT to undertake more languages. You’ll also be better equipped to meet the expectations of consumers who require content in their native language.

As one of the first language service providers to use MT, we have processed billions of words over the last two decades. That means you can trust our advice. Before incorporating MT into your localization strategy, be sure to:

  • Identify the right content for MT
  • Build the necessary linguistic assets to properly train your MT engine
  • Incorporate post-editing by humans to enhance MT quality when necessary

Use MT correctly to maximize cost savings without sacrificing quality.

A Brief History of Machine Translation

1954 - Georgetown researchers perform the first ever public demonstration of an early MT system.

1962 - The Association for Machine Translation and Computational Linguistics is formed in the U.S.

1964 - National Academy of Sciences forms a committee (ALPAC) to study MT.

1970 - The French Textile Institute begins to translate abstracts using an MT system.

1978 - Systran begins to translate technical manuals.

1989 - Trados is the first to develop and market translation memory technology.

1991 - The first commercial MT system between Russian, English and German-Ukrainian is developed at Kharkov State University.

1996 - Systran and Babelfish offers free translation of small texts on the web.

2006 - Google Translate’s statistical MT system is launched.

2012 - Google announces that Google Translate translates enough text to fill 1 million books every day.

2016 - Google enables neural machine translation (NMT) between eight languages, slashing word order mistakes by 50% and significantly improving its lexicon and grammar.

2020 - As of October, Google Neural Machine Translation (GNMT) supports 109 languages.

Review Some of the Most Engaging Content

The Future of Language Technology: The Future of Machine Translation

Machine translation will continue to evolve and become increasingly important for translation productivity if it is deployed properly.

Machine Translation vs. Machine Translation Plus Post-Editing

When is it best to rely on Machine Translation? When should you consider a hybrid model that incorporates traditional, human translation? We go through the scenarios. 

Neural Machine Translation: How Artificial Intelligence Works When Translating Language

Delve into what Neural Machine Translation is and why it is considered a game changer for the language industry. 

Machine Translation in Translation

This handy cheat sheet will bring you up to speed on the most important terms associated with Machine Translation. 

Selecting the Right Real-Time Translation Technology

Discover the differences between Lionbridge’s real-time translation technology and public Machine Translation engines in this guide. 

What is the Best Machine Translation?

Learn which Machine Translation engine will be most effective for your use case with Lionbridge’s Machine Translation Tracker. 

Selecting the right machine translation engine to best meet your needs is not easy. The quality of your source content, your source language and your target languages are among the factors that can influence machine translation engine performance. If you only had data to help you make decisions. Now, you do. Lionbridge’s Machine Translation Tracker measures the overall performance of major machine translation engines, performance based on language pairs and performance based on domains. See the quality scores of the four main machine translation engines during the past year.   

Meet Our Machine Translation Experts

Meet Our Machine Translation Experts

Rafa Moral

Vice President, Innovation 

Rafa oversees R&D activities related to language and translation. This includes initiatives pertaining to Machine Translation, Content Profiling and Analysis, Terminology Mining and Linguistic Quality Assurance and Control. 

Jordi Macias

Vice President, Language Excellence

Jordi is responsible for Language Excellence, which includes overseeing the Machine Translation team, the Language Quality Services team and the work Lionbridge does for some of its largest and most innovative clients.

Lionbridge’s machine translation can help you cut costs and reduce turnaround times. Get in touch to learn more.

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