Need More Than Business Cards?

April 4, 2014 by Matt Jacobs · Leave a Comment
Filed under: Translation Services 

In past ABLE blogs, we’ve taken a look at Translation Memory (TM) on a macro level.  As a recap, TM is a technology that provides assistance to a human linguist as they translate content.  By breaking down the text into smaller, more uniform parts (“segments”), the software builds a database of terms as they’re translated, and makes informed suggestions to the linguist based on their previous work.  This leads to improved consistency and quality, and passes along savings to the client by reducing redundant work.

Today, we look at another function of Translation Memory, which is its unique ability to identify and externalize code.  This allows linguists to ensure no content that should be translated isn’t, while protecting code so it doesn’t break during its re-importation at the conclusion of a project.

When ABLE and other language providers begin work on a new body of web, software or other code-based content, the first process is usually a review of the source files by an experienced localization engineer.  She or he compiles a configuration file (often in .ini format) that calibrates a Translation Memory tool to the specific code for translation.  It identifies tags, engineering notes and other text that shouldn’t be translated, as well as the unique quirks found in all code based on who engineered it.  This is then externalized by the tool; or in other words, made uneditable.

Once this configuration file is uploaded to the Translation Memory tool, the linguist can move through the client-facing content of a website – user interface, body text, help,  and the like – without having to proactively avoid both the obvious and unapparent background material found on the back end of an application.  This allows the linguist to focus on the job at hand.  And no engineer wants any linguist, regardless of technical prowess, monkeying with their code.

While configuration by a skilled localization engineer should yield files that can be cleanly imported after translation, it’s always best to perform some form of quality review before a release of software or web content.  Without broken code, it’s still possible for small sections of text to have been missed by a Translation Memory tool, especially in the case of images and other flat text features.  Once identified by a spot-checking linguist, these issues are usually quick and easy to resolve, and can be taken into account during the configuration process prior to future projects.

ABLE’s experts have years of experience handling complex web, software and other projects.  If you have any questions regarding the localization of your team’s code, we should be able to help.


Rise of the Machines?


Machine translation is a topic that’s come up a lot recently – not just in conversations with clients curious about cutting-edge translation solutions, but in other aspects of everyday life.  We hear about MT innovations in the media, find it embedded in web browsers and probably come across materials in our day-to-day lives that it has generated.  And practical applications aside, it’s a topic that people want to learn more about because it appeals to something basic in our lives – the urge to communicate; to hear and be heard.

As we’ve discussed in the past here on the ABLE blog, it’s our collective opinion that Machine Translation technologies are not yet mature enough to serve as a reliable translation solution for client-facing or otherwise quality-sensitive materials.  That said, for more informal uses, MT can be a useful and practical tool.

While a number of methodologies exist for MT, two leaders have emerged in terms of reliability and quality.

Rule Based Machine Translation:  RBMT translation systems are built largely using linguistic resources like bilingual dictionaries, grammar libraries and syntactic knowledge-bases to assemble translations.  The tools rely on the syntactic knowledge-base to assemble translations.  One of the primary benefits of RBMT is it can be used in conjunction with client-customized glossaries to enhance the translation’s subjective quality.

Statistical Machine Translation:  SMT translation systems are likewise built using existing bilingual dictionaries and grammar libraries.  Instead of using a set of fixed syntactic rules to translate, however, it relies on statistics and probability to select and apply the best grammatical approach.  The statistical approach is calibrated by referring to corpus, or a body of existing translations.

While both of these approaches have their benefits, a so-called hybrid approach is often taken that relies on a mix of Rules-based and Statistical-based principles to achieve the best results for a given client.

In previous blogs, we’ve discussed the drawbacks to MT when it comes to sensitive materials. That said, there are several uses for existing technologies.

Gist-translation of large bodies of content: It’s not uncommon for clients to have repositories of content that are not in need of perfect-quality translation, but still must be understandable for internal use.  Consider an international law firm with boxes of legal documents totaling over a million words.  Not every word of that material will be directly relevant to a case, but it’s critical to review the entire body of text to identify relevant information.  This first pass is an excellent use for MT.  Once the relevant areas of text have been identified, these can then be sent for human re-translation or editing.

First-pass translation: If source text is of a sufficiently literal and straightforward nature, Machine Translation can be adequate for the translation stage of client-facing materials.  This initial translation pass would then be followed by a human editing and quality assurance pass.  Technical manuals are often suitable for translation.

Ancillary translation:  Existing (and often free) MT solutions can benefit clients in many ways, and social media and collaboration clients may find these to be especially useful.  Since these platforms rely on the ability of users of all linguistic backgrounds to interact and share content they themselves have created, formal human translation is impractical or entirely impossible.  This creates a frustrating challenge –it’s easy and affordable to reach out to new markets by simply translating a site’s User Interface, help and other in-house content through traditional methods; but it’s often not worth it if it simply creates a body of segregated linguistic communities.  However, with embedded, easy-to-use tools like Google Translate at everyone’s fingertips, the ability for end users to connect across linguistic boundaries is just a click away.

If you have questions about how Machine Translation may be right for you, ABLE’s highly-experienced team of experts is here to help.  We look forward to discussing your team’s specific challenges and suggesting a customized solution using one or more of these approaches.



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