Machine Translation Quality Evaluation Services

Machine Translation Quality Evaluation Services

From Google translate to Facebook's automatic translation of posts, machine translation is increasingly becoming part of our everyday life. As machine translation is emerging as a popular mode of translation, its quality and acceptability are increasingly gaining importance. Judging and measuring translation quality is called machine translation quality evaluation. SunTec.AI offers both automated and manual machine translation evaluation services. While manual evaluation of machine translation is high on quality, automatic machine translation evaluation involves lower costs. Also, manual evaluation is comparatively slower and lacks repeatability. Evaluation of machine translation by humans is generally trusted as the gold standard. Whereas, automatic evaluation aims to produce results that are as similar as possible to human judgements.

SunTec.AI’s Manual Machine Translation Quality Evaluation Services

Human evaluation is the most popular option for judging and measuring the quality of machine translation. At SunTec.AI, our experts in translation and linguists evaluate machine translation outputs from two different perspectives. The translation experts evaluate the degree of adherence to the target language norms and target text to check for the fluency in translation. On the other hand, our experienced linguists check for source text adherence. They ensure how accurately the translated text represents the content of the source text. So, our linguists check for accuracy and translation experts check for fluency. Our evaluators are bilingual i.e. they are experts in both the source and target languages.

SunTec.AI’s Automatic Machine Translation Quality Evaluation Services

When compared to manual evaluation, automatic evaluation is cheaper and fast. Automatic evaluation systems evaluate the quality of machine translation on some metrics. These metrics for automatic evaluation need human reference translation. The metrics compare MT output with high-quality human translations. Since human translations have great variability, to ensure the accuracy and fluency of the machine translation, it is necessary to have large volumes of human reference translations for each machine-translated sentence. Experts at SunTec.AI use widely acceptable machine translation evaluation metrics like BLEU, NIST, METEOR, and TER. With a large pool of linguists, language experts, data contributors from various language backgrounds, SunTec.AI has the necessary resources to cater to the demands of reference translations of any scale.

Why Choose Us?

While machine translation (MT) evaluation by automatic means is cheaper and quicker than human evaluation, they lack quality. With our high quality and large reference translations, you can improve the accuracy of your automatic evaluation algorithms. Also, with over 20 years of experience in human evaluation of machine translation, SunTec.AI has become a trusted partner of many companies worldwide.

  • ISO 9001:2015 Certified
    For Data Quality

  • ISO 27001:2013 Certified
    For Information Security

  • 20 years of

  • Cost-effective

  • Scalable

  • Quality

SunTec.AI provides you:

  • 20+ years of experience
  • Experienced translation experts and linguists
  • High-quality and multilingual reference translation datasets
  • Superior quality machine translation evaluation services
  • Large pool of data contributors from diverse backgrounds
  • Scalable output
  • 99%+ accuracy
  • Cost-effective solutions
  • Quick turnaround time
  • ISO 9001:2015 certified for Quality Management
  • ISO 27001:2013 certified for Information Security Management
  • 24*7 customer support

Discuss Your Project With Us?

To discuss your machine translation quality evaluation requirements and understand more about our manual and automatic machine translation evaluation services, contact us at


We understand that the level of detail applied during data annotation directly impacts the overall accuracy and quality of the resultant AI algorithm’s predictions.

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