Quality frameworks

Is there a common denominator for content quality? Frameworks for evaluating both human and machine translation are available to support benchmarking.
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Service description

You want to know what you are paying for. Rate your vendors. Select the best MT engine or translation workflow for your content and target languages. Define the scope of post-editing. Monitor quality against agreed service levels.

Various techniques are available for MT quality estimation, ranging from automated methods to manual linguistic QA, though their results aren’t always comparable. A quality framework based on error typology helps to reduce subjectivity and eliminate bias in evaluations. The resulting scores can be combined with edit distance and time for comparable aggregate metrics.

Why choose espell?


espell uses a comprehensive framework adapted to different use cases. Error categories remain consistent, but their assigned weights reflect specific criteria for any given content type.


Our experience with assessing and improving the quality of transcreated content is distilled into a QA matrix. This addresses aspects like creative adaptation, tone of voice, fluency, culture, etc.


For MT post-editing, different service levels are defined based on the standard error categories. This helps to scope and limit the MT PE effort to the key quality criteria.


Our pricing is always custom-tailored to your needs.

We apply industry best practices to provide transparent and adaptable metrics and pricing.
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"When it comes to machine translation, there is a wide range of possible implementation strategies. One of the world’s top 50 SaaS companies chose espell to design an end-to-end process to localize their help documentation. In addition to architecting a fully automated process built on the client's infrastructure, espell’s Technology and Language Solutions team created a customized and cost-effective multi-engine MT solution with special focus on content specific configuration, engine selection and injecting terminology. The result is a fast, seamless, continuous localization process with linguistically optimized machine translation output."

Andrea Téry
head of technology and language solutions

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