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result = evaluator.evaluate(
    eval_templates="translation_accuracy",
    inputs={
        "input": "Hello, how are you?",
        "output": "¡Hola, cómo estás?"
    },
    model_name="turing_flash"
)

print(result.eval_results[0].output)
print(result.eval_results[0].reason)
Input
Required InputTypeDescription
inputstringContent in source language.
outputstringContent in translated language.
Output
FieldDescription
ResultReturns a score representing the translation accuracy, where higher values indicate superior translation quality.
ReasonProvides a detailed explanation of the translation accuracy assessment.

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What to Do When Translation Accuracy Evaluation Gives a Low Score

Reassess the evaluation criteria to ensure they are well-defined and aligned with the evaluation’s objectives, making adjustments if necessary to enhance their comprehensiveness and relevance. Analyse the translation for semantic accuracy, cultural appropriateness, and natural linguistic expression, identifying any discrepancies that may affect meaning. If inconsistencies are found, refine the translation to ensure it accurately conveys the original intent while maintaining contextual and cultural integrity.

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Differentiating Translation Accuracy with Factual Accuracy

Translation Accuracy focuses on ensuring that the meaning and context of the input are accurately conveyed in the output, while Factual Accuracy verifies whether the output contains factually correct information based on the provided context. Translation Accuracy assesses semantic accuracy, cultural appropriateness, and preservation of meaning, Factual Accuracy evaluates the correctness of factual statements relative to the given context.
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