What is the role of the BLEU metric in evaluating machine translation?
BLEU (Bilingual Evaluation Understudy) is an automated metric used to measure the quality of machine translation output by comparing it to one or more high-quality human reference translations. It primarily quantifies the level of overlap in n-grams (sequences of words) between the machine output and the references.
BLEU calculates a precision score, focusing on how many n-grams (typically 1 to 4-grams) in the machine translation appear in the reference translations. It applies a brevity penalty to penalize translations significantly shorter than the references. Key considerations include that BLEU relies heavily on the quality and representativeness of the reference translations, it primarily measures adequacy (presence of correct content) over fluency, and its correlation with human judgment is strongest when averaging scores over a large corpus, not for individual sentences.
BLEU provides a fast, consistent, and inexpensive method to automatically track the performance of MT systems during development, optimization, and research. It enables rapid comparison of different models or system iterations. While imperfect as it doesn't fully capture fluency or meaning, it is widely valued as a practical benchmark and useful indicator of progress when used alongside human evaluation.
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