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Translation Error Rate (TER)

Translation Error Rate (TER) is a metric used to assess the accuracy of machine translation by calculating the number of edits (insertions, deletions, substitutions, and shifts) needed to make a translated text match a human reference translation. A lower TER means higher translation quality.

Why it’s important:

  • Helps compare machine translation models to improve accuracy
  • Identifies errors in AI-generated translations
  • Supports post-editing workflows by measuring effort required to correct translations
  • Enhances translation quality assurance in localization projects

Real-world example:

A tech company testing AI translation software:

  • Runs translations through the model and compares them to human-translated text
  • Uses TER to measure how much editing is needed to match the human version
  • Adjusts the model to reduce TER and improve translation accuracy

 

 

This article is about:

  • Definition:
    Translation Error Rate (TER) measures how many edits are needed to correct a machine translation
  • Industry relevance:
    Used in AI translation, localization, and quality assessment to improve machine learning models
  • Use case:
    Tech companies use TER to refine AI-driven translation tools and enhance accuracy

By tracking TER, businesses and researchers optimize machine translation performance and improve multilingual communication.