Skip to main content
Accurate translation is essential for ensuring that content is linguistically precise, contextually appropriate, and culturally relevant across different languages. High-quality translations must preserve the original meaning, intent, and tone, while also adhering to the grammatical and stylistic conventions of the target language. Challenges associated with poor translation quality include:
  • Loss of Meaning – Misinterpretation or distortion of the original message.
  • Cultural Insensitivity – Use of phrases or expressions that do not align with cultural norms.
  • Unnatural Language – Translations that are overly literal, mechanical, or awkwardly phrased.
To assess translation effectiveness, Translation Accuracy Evaluation measures how well a translation conveys the original meaning while ensuring linguistic fluency and cultural appropriateness. Click here to read the eval definition of Translation Accuracy

​
a. Using Interface

​
Required Inputs

  • input: The original text.
  • output: The translated text.

​
Configuration Parameters

  • Check Internet: Whether to verify information using external sources for contextual validation.

​
Output

  • Returns a score between 0 and 1, where higher values indicate superior translation quality.

​
b. Using SDK

Export your API key and Secret key into your environment variables. 
result = evaluator.evaluate(
    eval_templates="translation_accuracy",
    inputs={
        "input": "I would like a coffee, please",
        "output": "Ich möchte bitte einen Kaffee."

    },
    model_name="turing_flash"
)

print(result.eval_results[0].output)
print(result.eval_results[0].reason)