BLEUROUGECIDEr

BLEUROUGECIDEr is a term sometimes used in natural language generation to refer to a proposed composite evaluation score that combines three established metrics: BLEU, ROUGE, and CIDEr. There is no single official metric by this name, and implementations or formulations vary across studies. The idea behind such a composite is to balance the complementary signals provided by the individual measures rather than rely on any one metric alone.

BLEU is a precision-focused metric that measures n-gram overlap between a candidate text and reference texts, with a brevity penalty to discourage overly short outputs. ROUGE emphasizes recall by comparing overlaps with references, and ROUGE-L and other variants account for longest common subsequences and other factors. CIDEr focuses on consensus with references using TF-IDF weighting of n-grams to reduce the influence of generic phrases and to better reflect human judgments in tasks like image captioning.

In a BLEUROUGECIDEr-style score, each metric would typically be normalized to a common scale and combined, often through a weighted average. The exact weights and normalization method can significantly affect the result, and there is no universally accepted standard for how to calculate the composite. Consequently, researchers advise interpreting such a score with caution and in the context of reporting the underlying individual metrics as well.

Advantages of a composite approach include potentially greater robustness by leveraging multiple aspects of text quality. Limitations include dependence on reference quality, task specificity, and the challenge of choosing appropriate weights. In practice, most studies report BLEU, ROUGE, and CIDEr separately and supplement with human evaluations.