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:
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.
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.
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.
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.
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