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Commercial MT Users

Since machine translation started to be commercially available, commercial users of machine translation had the challenge to identify use cases where MT increases value for the respective company. Over the years, three major use case categories seem to have evolved:

  • MT for big data
  • MT for workflow optimization
  • MT as part of a translation service

Below, we collected some of these use case stories. Each of these categories have different criteria, sometimes the criteria are unique to the actual use case.
Please do not hesitate to send us additional stories that you might have.

The category of use cases that try to cope with the “big data” challenge is mainly trying to make large data available to more potential consumers of these data. The criteria on whether MT helps or not is directly derived by the behavior of the millions of users if interacted with content that was translated. But in any case, the financial impact is measured finally in “revenue”.

Google – case study
Google Translate is a translation service that provides instant translations between dozens of different languages. It can translate words, sentences and web pages between any combination of our supported languages. With Google Translate, we hope to make information universally accessible and useful, regardless of the language in which it’s written. When Google Translate generates a translation, it looks for patterns in hundreds of millions of documents to help decide on the best translation for you. By detecting patterns in documents that have already been translated by human translators, Google Translate can make intelligent guesses as to what an appropriate translation should be. This process of seeking patterns in large amounts of text is called “statistical machine translation”. Since the translations are generated by machines, not all translation will be perfect. The more human-translated documents that Google Translate can analyse in a specific language, the better the translation quality will be. This is why translation accuracy will sometimes vary across languages.
http://googletranslate.blogspot.com
eBay – case study

eBay has developed its very own machine translation tools to help fuel expansion in Russia. The tools allow Russian buyers to get accurate translations of eBay seller listing and communications in real time, making working with eBay much easier for international transactions. In 2013, eBay began to use machine translation in the eBay environment to enable people across country boundaries to communicate and deal with each other. eBay rolled out their first machine translation technology in January, translating Russian to/from English in real-time. Machine translation is used to match user queries and products in the inventory across languages (query translation), and to present the results to the user translated into the language of the user’s original query. This opens up new avenues for sellers around the world to reach global buyers, across language barriers. Currently, eBay translates millions of queries and query results daily per language.

http://blog.ebay.com/machine-translation-at-ebay/
Microsoft – case study
Bing Translator (previously Live Search Translator and Windows Live Translator) is a user facing translation portal provided by Microsoft as part of its Bing services to translate texts or entire web pages into different languages. All translation pairs are powered by the Microsoft Translator statistical machine translation platform and web service, developed by Microsoft Research, as its backend translation software. Two transliteration pairs (between Chinese Traditional and Chinese Simplified) are provided by Microsoft’s Windows International team.
http://blogs.msdn.com/b/translation/
Facebook – case study
Facebook’s latest acquisition could help it connect users across language barriers. It has just announced that it’s acquired the team and technology of Pittsburgh’s Mobile Technologies, a speech recognition and machine translation startup that developed the app Jibbigo. From voice search to translated News Feed posts, Facebook could to a lot with this technology.
Facebook tells me “We’ll continue to support the [Jibbigo] app for the time being.” Jibbigo launched in 2009, and allows you to select from over 25 languages, record a voice snippet in that language or type in some text, and then get a translation displayed on screen and read aloud to you in a language of your choosing.
http://jibbigo.com/index.html

MT for workflow optimization:

For the category of workflow optimization, the main criterion is “cost”, but also “revenue”. To achieve that, MT use is put in the different workflows to minimize human involvement and therefore cost, or to increase speed of translation and therefore generating a competitive advantage. In some cases, the use of own technology is preferred, either in-house development, building on top of open source technology, or by using third-party MT tools. The use cases in this category can be from a fully automated translation service (MT service), over human-aided machine translation (HAMT service), to machine-aided human translation (MAHT service).
Adobe – case study
Adobe now uses Machine Translation plus post-editing as the default localization process for all of its product documentation and most of its UI.  The MT engines are customized using in-house translation memories, and post-editing is charged at a discounted rate as compared to translation from scratch.  The discounts are set by calculating the amount of time saved by post-editing.
http://blogs.adobe.com/globalization/more-content-into-more-languages/
TransPerfect – case study
TransPerfect’s proprietary methodology combines machine translation with complementary technologies and human translators to quickly and reliably produce usable translations for review of large volumes of text, all completed in a fraction of the time that would be required for a standard translation process. While machine translation quality falls far short of human translation, if used properly in conjunction with human reviewers, the utility of machine translation can be stretched to include both non-distribution applications including document review, legal discovery, and internal correspondence, as well as distribution-level materials for which extremely fast turnaround times are a requirement.
https://www.transperfect.com/services/translation_machine.html
Etsy – case study
Etsy, an e-commerce marketplace specializing in vintage, art, and handmade goods, recently released a test integration of Machine Translation on user product listing.  Listings in a small set of non-English languages are automatically translated into English, allowing those sellers access to a much wider audience of buyers.  The hypothesis guiding the test is that although commercial transactions is an area where extremely high translation quality is traditionally required, the Machine Translation can provide enough understanding that users feel confident to complete the transaction.
https://blog.etsy.com/news/2013/translating-the-marketplace-in-more-ways-than-one/

 

MT as part of a translation service:

Machine translation as a service can be either a byproduct for some teams and companies that develop MT technology for above mentioned use cases, or they focus on MT technology development, that customers then use for these use cases. The use cases in this category can also be from MT service, over HAMT service, to machine-aided human translation MAHT service.

MT as a service is offered in different business models:

  1. The oldest model is the machine-based perpetual software license (MBPSL). This model allows the user to use the machine translation service (most of the time on hardware the user has to purchase separately) from the time the license is purchased. Maintenance contracts allow the user to receive updates and enhancements timely and either for free or for a discounted price.
  2. The second model is the machine-based annual software license (MBASL). This model allows the user to purchase a license similar to the machine-based perpetual license, but the license is constrained to only one year, and has to be renewed or extended annually. The advantage of this is that the upfront cost is lower, the disadvantage is that the the total cost is usually higher than MBPSL plus maintenance, if the user decides to stay in the same licensing model.
  3. The third model is the volume-based service usage pricing (VBSUP). This is a service, where a translation service is offered, and the pricing is by word, by character, by byte or another unit. Other dimension is often to allow the user to pick and choose the expected quality, e.g. by allowing different levels where the human is in the loop, or by using more hardware resources for each translation (and higher cost at the provider’s side, therefor the higher cost to the consumer).

Google Translate

Google Translate is a translation service that provides instant translations between dozens of different languages. It can translate words, sentences and web pages between any combination of our supported languages. With Google Translate, we hope to make information universally accessible and useful, regardless of the language in which it’s written. When Google Translate generates a translation, it looks for patterns in hundreds of millions of documents to help decide on the best translation for you. By detecting patterns in documents that have already been translated by human translators, Google Translate can make intelligent guesses as to what an appropriate translation should be. This process of seeking patterns in large amounts of text is called “statistical machine translation”. Since the translations are generated by machines, not all translation will be perfect. The more human-translated documents that Google Translate can analyse in a specific language, the better the translation quality will be. This is why translation accuracy will sometimes vary across languages.

http://googletranslate.blogspot.com

Microsoft Translator

Bing Translator (previously Live Search Translator and Windows Live Translator) is a user facing translation portal provided by Microsoft as part of its Bing services to translate texts or entire web pages into different languages. All translation pairs are powered by the Microsoft Translator statistical machine translation platform and web service, developed by Microsoft Research, as its backend translation software. Two transliteration pairs (between Chinese Traditional and Chinese Simplified) are provided by Microsoft’s Windows International team.

http://blogs.msdn.com/b/translation/

SDL BeGlobal

With SDL BeGlobal, business users are now enabled to manage secure, global and trusted communications with customers in real-time through one central interface for multiple types of content, communication and social media while significantly reducing their translation costs. The application can also be used by translation teams to increase productivity for content that requires human translation or review or reduce post-editing costs through crowd-sourcing.

http://www.sdl.com/products/sdl-beglobal/

SYSTRAN

SYSTRAN’s SYSTRANLinks is a comprehensive website-translation solution, it offers an online CMS platform that lets you launch and manage your localization projects with unprecedented ease – all from an intuitive centralized base. And it gives you a rich range of user-friendly tools that allow you to constantly enhance your content and manage the entire localization process more easily.

http://blog.systranlinks.com

IBM

IBM® WebSphere® Translation Server for Multiplatforms is a machine translation (MT) offering that can help companies remove language as a barrier to global communication and commerce. It allows you to leverage your existing web infrastructure to provide content to users in their native language at a lower cost than traditional translation.

WebSphere Translation Server for Multiplatforms:

– Provides a robust, scalable platform for providing content in multiple languages.

– Offers advanced MT features to accelerate translation, lower costs and help improve customer service.

– Integrates with your existing web infrastructure to help simplify and lower the costs of integration and implementation efforts.

http://www.ibm.com/software/products/en/translation-server

Some References and Reads:

  • http://www.commonsenseadvisory.com/Default.aspx?Contenttype=ArticleDetAD&tabID=63&Aid=3025&moduleId=390
  • http://www.wired.com/insights/2012/09/end-users-big-data/
  • https://www.taus.net/executive-forums/exploring-the-intersection-of-big-data-and-machine-translation
  • http://www.businesscomputingworld.co.uk/translating-the-big-data-language-problem/
  • http://www.dataversity.net/big-data-translation/

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