In a language quality review program, subject-matter experts examine your translation team’s work and flag problems for correction. But how do these reviewers ensure their feedback is objective, precise, and quantifiable? That’s the role of language quality metrics.
Such metrics provide a standardized rating of your vendor’s work, weighing multiple factors to provide a language quality score. As a result, you gain clear, consistent benchmarks to assess quality and improve performance. At the same time, a strong set of metrics allows for plenty of customization to your specific needs.
What’s the Best Way to Measure Language Quality?
For a flexible yet powerful way of measuring language quality, reviewers can turn to a varied set of frameworks such as Multidimensional Quality Framework (MQM) and Dynamic Quality Framework (DQF). Within these systems, a reviewer typically identifies and classifies errors by looking at three key dimensions:
In addition to these core metrics, reviewers may consider other, optional measures of quality if these are relevant to the specific client or task. For example:
When reviewers flag a problem, they can note not just the kind of error, but also its severity—minor, major, or critical. The number, type, and severity of errors all contribute to the translation’s overall quality score.
Putting a Number on Language Quality
To calculate the quality score, a mathematical formula can be used to rate the translation on a scale of 1 to 100. This is a dynamic process, making use of quality assurance tools to score the text in real time as reviewers identify and flag errors.
In scoring a translation, every error discovered receives a numerical value that depends on a range of different variables. However, the importance the reviewer assigns to these variables isn’t set in stone. Instead, reviewers can tailor their weighting to meet different priorities and goals.
As reviewers detect and score the errors in a text, they divide the total value of all these quality issues by the number of words in the content under review. They then multiply this figure by 100 to produce the overall quality score.
In assessing the importance of quality scores, the client sets the threshold for success. For instance, a company may consider 80 or more to be a passing grade for one type of content or goal—but anything below 99 might count as a failure for another. In this way, the company can gauge its translation team’s relative performance at different tasks.
Why Metrics Matter—Now More Than Ever
How do you benefit when your language quality review team uses the right metrics and scoring? Such measurements enable you to achieve multiple goals that enable more successful localization.
Assess, calculate, and compare language quality across different types of content and tasks. Well-developed metrics provide you with a consistent, customizable standard for assessing the quality of all translated content. That gives you a more accurate view of your vendor’s performance, so you can make more informed decisions about resource allotment and strategy.
Track how your translation vendor’s work is trending over time. As the language review team processes more and more content, a QA software tool can graph the results and present them in a dashboard for your review—so, for example, you can see how quality has been improving or declining in the last six months. In this way, you can better understand whether your translation projects are heading in the right direction.
Improve the quality of machine translations. More and more, vendors are using software to translate their clients’ content—but the results of machine translation are only as good as the data behind them. By converting the judgments of human experts into quantitative metrics, your review team provides data that can be used to train and optimize a translation program’s algorithms.
As machine translation becomes the norm, companies may be able to produce greater amounts of localized content, aimed at more audiences than ever before. This increased output will only add to the challenge of quality assurance—and it will become all the more important to use metrics that work.
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