Is BLEU only suitable for translation tasks?
No, BLEU is not exclusively suitable for translation tasks. While originally designed and predominantly used to automatically evaluate the quality of machine translation (MT) outputs, it has found application in other natural language generation tasks.
BLEU measures similarity to one or more human-written reference translations by comparing overlapping n-grams (contiguous word sequences). Its primary value lies in providing a scalable, automated benchmark correlating with human judgment, particularly for fluency and phrase matching. However, it relies heavily on high-quality reference texts and struggles to accurately capture semantic meaning or adequacy compared to the source. Its effectiveness diminishes for tasks requiring high creativity or significantly different outputs from the references.
Beyond MT, BLEU has been adapted as an auxiliary metric for evaluating text summarization quality, checking the correspondence of generated summaries to reference summaries. It serves as a convenient standard benchmark enabling rapid, cost-effective comparison of system performance. Its main business value is offering quantifiable, automated comparison of output quality against a defined standard reference, facilitating iterative development cycles.
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