Which method is utilized in machine translation to compute a loss metric?

In machine translation, the method commonly used to compute a loss metric is the BLEU (Bilingual Evaluation Understudy) score. BLEU is specifically designed to evaluate the quality of text generated by machine translation systems compared to one or more reference translations. It measures the overlap of n-grams (contiguous sequences of n items from a given sample) between the candidate translation and reference translations, providing a quantitative score that indicates how closely the generated output matches human translations.

BLEU is particularly valuable in the context of machine translation because it emphasizes both precision and recall, accounting for variations in translation while still encouraging accurate representations of the source text. The score ranges from 0 to 1, where higher values indicate better translation quality.

Other methods mentioned, like Jaccard Similarity and F1 Score, are more generalized evaluation metrics applicable to various classification tasks but are not specifically tailored for machine translation assessment. Cross-Entropy Loss, while a common loss function in training deep learning models, focuses on measuring the performance of a classification model whose output is a probability value between 0 and 1, and it does not directly evaluate the quality of translations in the way that BLEU does. Thus, BLEU is the most suitable method for computing a loss metric