Fast translation services are a function of many factors: How fast the translation is assigned to translators, how many translators are involved, how fast translators translate and once the translation is completed, how fast the client receives it.
A very important factor in facilitating fast translations is what translation environment is used. Many tools are out there, but can possibly generate very bad quality (aka machine translation). Translation Management Systems (TMS) with a powerful Translation Memory engine can however facilitate the translation while improving quality. Their use is a critical component of getting a translation fast to the client, without sacrificing quality.
Have you ever wondered how translation memory (TM) tools work? Did you ever use them and run into issues and wondered why these issues surface?
Like for instance why do you get non-100% matches when you analyze a file that you just finished fully translating? Or why MS-Word word count is different than your TM tool’s count? Or why fuzzy matches are calculated the way they are?
To really understand TM tools, one has to take a closer look under the hood. Here is a summary to help you see how the proper translation memory choice can expedite translations.
Translation Memory Key Components
All TM tools contain these 4 essential parts: parsers, segmenter, fuzzy match engine and graphical user interface (GUI):
1. Parser: Source files need to be parsed correctly by the tool to start the process. This means that the TM tool has to read files programmatically and correctly dissect them into external code, text to translate, and internal code to the translatable text. An example of external code could be the numerations that you see in this blog. An example of internal code is a font typesetting, like bold or italic.
What complicates parsers are the different file formats that are used, like XML, HTM, InDesign, FrameMaker, MS Word, Excel, PowerPoint, MS Publisher, PageMaker, QuarkXPress, PHP, Java, RC… Most authoring tools are migrating to support XML, but still require custom formats. XLIFF can make this easier, but it has not gained much traction yet among authoring tool providers.
The more formats the translation tool supports, the faster the translator can get started on her translation.
2. Segmenter: This is the language-tuned engine that determines where to break the segment. Typically segments end after a period “.” or a paragraph ending, like a hard line return. For instance, headers, titles and callouts, all are independent segments that don’t necessarily end with a period. The title above for instance, is a full segment that should be translated independent of the sentence that follows it.
A good segmenter will contain as many of the exception rules that are needed for each source language that it supports. For instance, it is important for it to discern between an end of segment period and a period used in abbreviated words or numeric notations. Numbers such as “2.0” or abbreviations like “misc.” will therefore not break the segment into incomplete sentences. In short, segmenters should be smart and tuned for each language to provide the translators full sentences to translate, or else, the resulting translation risks being grammatically incorrect.
The more fine tuned the segementer is for each source language, the less work translators have to do to get the translations correctly logged in the translation database and the more accurate the database is, facilitating future updates and speeding up translations!
3. Fuzzy match engine: This is the mathematical brain of the tool that helps find the closest match in the translation database and set the necessary penalties to the match. This is done to accurately compensate the translator for the effort needed to adapt the fuzzy match into a correct translation, and to accurately estimate the time needed to perform the work.
Good fuzzy match engines are fast, robust, scalable and accurate. They don’t simply compare characters or bytes— they compare words and strings of words at a time. They also have built-in intelligence to identify the changes that are relevant and the ones that are less relevant to the translator. For instance, an internal tag change, a punctuation change or an extra space may have an impact on the target sentence; they are however not as important as changing “dig” into “dog”!
The more accurate the fuzzy match engine is, the better choices translators are given from the translation database to reuse, the faster they can complete their translations accurately!
4. Graphical user interface: It is no secret that translation tasks continue to be the critical path of any localization project and that the more efficient the translator becomes the less costly and faster it is for him or her to deliver. Here, the GUI plays a big role in making the entire environment useful to the translator, or not.
Ideally, all project assets including the translation memory, the terminology database, the query database and machine translation options, should be at the fingertips of translators for them to become as efficient as possible while performing their work. With the advent of online translation environments, achieving this is within our reach.
The more intuitive the user interface is and the more automation is built in for terminology search, dictionary lookup, grammar and spell check… the faster translators can complete their tasks.
Achieving Fast Translation
Perhaps there is room for free or inferior tools for beginners or non-professional translators. For professional translation providers however, time is of essence. Using inferior tools require spending extra time to properly parse a file, or to correctly segment it, or to accurately analyze it, then to complete its proper translation, precisely finalize and format it, then to adequately export or manage the translation memory…
If you are seeking fast translation services, look under the hood of your supplier’s translation tools; the better the engine, the sooner and more reliably you will get your translations!
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