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Automated translation quality control
Nathalie De Sutter discusses how new technologies can mitigate the threats faced by Language Service Providers (LSPs). All maturing industries try to avoid their products being seen as commodities that can easily be bought from any supplier; localisation and translation service providers are no exception. In most cases, translation does not pertain to the core business of the customer, who therefore considers it to be a ‘non-critical’ purchase. This, combined with the decreasing complexity of the supply market, increasing competition and more professional purchasing behaviour, results in the perception of localisation as a commodity. Customers demand greater flexibility, with higher volumes to be processed and shorter deadlines. One indicator is the practice of auctioning assignments of translation projects. Differentiate
or innovate Kaizen™ revisited What more has Kaizen taught us? First of all, quality is the responsibility of everyone in the organisation and not exclusively of the quality department. Indeed, fighting non-value-adding activities, or Muda, should be one of the key activities of all members of organisations striving for continuous improvement or Kaizen. A second insight relates to the fact that quality improvement, contrary to traditional belief, has a cost-reducing effect. ‘Doing it right the first time’ may require an initial investment, but the long-term impact generates many advantages outside the limited framework of quality. Spending money on quality seems inevitable; how much money, however, depends on when you intend to spend it. Go
to Gemba Doing
it right the first time Automated
translation quality control Figure 1. Error message generation on an omitted translation QA Distiller™ also checks for a number of language-dependent formatting irregularities. It will, for example, verify whether a translation contains any characters that do not pertain to the valid Unicode range of the language in question. It checks whether punctuation is correct, whether numbers are identical in the source and target (and are correctly formatted), and that no suspicious multiple spaces and tabs occur in the text. Last but not least, it contains a terminology checker that verifies whether each term in the source text has been translated in accordance with one or more loaded dictionaries. It is true that these errors only indicate the more ‘formal’ translation mistakes. Other possible problems such as style, fluency, register and grammar are ignored, making this quality checker suitable only for texts that are repetitive or deal with highly specialised subjects, a point that is equally applicable to the use of translation memories in general. In addition, proofreading may still be necessary. Formal mistakes, however, are usually indicative of other severe quality issues in a translation and their detection allows us to evaluate our suppliers more objectively. Vendor evaluation and selection is, after all, one of the most critical activities in a business model that depends to a large extent on outsourcing. Discussions about style preferences or the choice between equally correct terms have often led to never-ending discussions, whereas formal mistakes such as inconsistent translations (Figure 2), untranslated text and incorrectly formatted numbers (for example 0.12 instead of 0,12 in French) are objectively incorrect in technical manuals. Total quality assurance obviously requires an integrated approach, but every step toward a better translation is progress. Figure 2. Inconsistent translation A check with QA Distiller™ also works for the verification and cleanup of translation memories, thus preventing the dreaded ‘garbage in, garbage out’ effect that should always be taken into consideration when previous translations are recycled. Some errors will be generated only if there is a discrepancy between source and target text (Figure 3). QA Distiller is basically a comparison tool, based on the assumption that there is a certain level of correspondence between the source and target text. This implies that, if the source text itself contains errors or ambiguities, QA Distiller™ may not be able to identify related mistakes in the target text. This takes us to another discussion on which I will comment very briefly: clients should not penalise translation suppliers for errors that are caused by faulty source documents. Figure 3. Terminology mistake Reducing
proofreading efforts Figure 4. Implementing QA Distiller™ in the localisation process Get
what you expect A well-defined division of tasks and an open communication between you and your (freelance) supplier are equally vital. Figure 4 illustrates how tasks may be allocated. The translator runs a spelling and grammar check on the translation before delivering it, while the translation company commits itself to verifying the quality of a translation memory before forcing the freelancers to give discounts on recuperated segments that may still require complete revision and editing. Figure 5. Parsing bilingual files in ColourTagger™ Kaizen
in translation Nathalie
De Sutter holds a masters degree in Indology
and a post-graduate degree in Business Communication.
She is based in Ghent, Belgium, and works as a Business
Development Manager at YAMAGATA
EUROPE.
The company was founded in 1998 as the centre of excellence
for multilingual content management within the Japanese
Yamagata Printing Group.
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