Machine Translation (or MT) is the ability of a computer to translate and render one language into another, without human contribution. The process sounds straightforward, the meaning of one text (the original source) must be reestablished in a target language, but translation is often more complex than a simple word-for-word transition. General categories of machine translation properties are detailed as follows:
Generic Machine Translation
GMT is designed as a ‘one size-fits-all’ policy for consumers or anyone wanting on the spot translations of short materials.
Customized Machine Translation
CMT involves translation software that can target language belonging to a specific requirement (such as an industry or organization). This offers a higher level of accuracy for high volume translations.
Enterprise Machine Translation
EMT incorporates augmented translation engine strategies that meet demands for high volume and high velocity content translation as well as multi-lingual communications.
In addition to the different categories of Machine Translation, there are distinctive systems developed to meet such translation demands. Some of the more commonly known systems are as follows:
Rule-Based Machine Translation
Using a large database of pre-defined rules established by human experts, RBMT can deliver fairly predictable translation results. The database typically contains a refined set of grammatical and syntactical rules, as well as a specialized, robust dictionary for use in the specific industry or discipline that requires translation needs. A key benefit of RBMT is that it produces quality translations for language pairs with very abstract word order rules.
Statistical-Based Machine Translation
Working through the use and premise of probabilities, with computer algorithms that analyze the functions of language across a large body of existing multilingual corpora, SBMT works in a manner of “learning” a language in order to translate by way of applying statistical modeling- all in a more cost-effective platform. SBMT generates a higher fluency conversion than RBMT, but at the cost of being less consistent in output.
Neural Machine Translation
Purported to be the next generation of SBMT, NMT works at a higher level than word-by-word conversion, and analyzes whole sentence structures to calculate the statistical probability of factors such as word choice, grammar, and syntax, to then generate relevant translations. Google is currently pioneering their deep learning techniques in combination with their web-based translation services, with a reported 35% of all general machine translation queries to Google being served by their NMT systems.
A trustworthy Language Service Provider knows exactly how to leverage Machine Translation functions, and combine this output with the unchallengeable expertise of human translators. At CSOFT we are committed to using the best strategies to ensure no compromises are made to your message. If you value authenticity, and aspire for fluent, global communications, talk to us now and we’ll get your words out to the world.
Written by Erin Strong – Senior Technical Writer at CSOFT International
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