BLEU score is the correct option for this task because it provides an objective, language agnostic way to compare machine translations to one or more human reference translations by measuring n-gram overlap and applying a brevity penalty.

BLEU score works by computing precision over n-grams between the candidate translation and reference translations and it penalizes overly short outputs with a brevity penalty. It supports using multiple human references which improves reliability for languages like Spanish and Japanese and it is widely used as a baseline automatic metric for translation quality.

ROUGE metric is not ideal because it emphasizes recall and is commonly used for evaluating summaries rather than direct translation fidelity.

F1 measure is meant for classification tasks where precision and recall over discrete labels are combined and it does not capture the sequence or fluency aspects needed for translation evaluation.

Root mean squared error is a regression metric for numeric predictions and it has no direct applicability to comparing generated text to reference translations.

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