The three tasks require different evaluation metrics because they assess different characteristics of model output.

For machine translation, the correct metric is BLEU . AWS describes BLEU as a metric that “Measures n-gram overlap, focusing on precision.” AWS identifies machine translation as its typical use case. BLEU compares sequences of words or tokens in a candidate translation with one or more reference translations. Higher agreement of relevant N-grams generally produces a better score. Therefore, the first task maps to B .

For semantic similarity, BERTScore is appropriate. AWS explains that BERTScore uses a BERT-family model to create sentence embeddings and compares them using cosine similarity. Unlike simple lexical overlap, this allows semantically related phrases to receive similarity credit even when the exact words differ. Thus, measuring semantic similarity between a generated response and a reference response maps to A .

For a binary classifier, Precision evaluates the reliability of positive predictions. AWS defines it as the fraction of actual positive instances among the examples predicted as positive. In formula form:

Precision = TP / (TP + FP)

A high precision score means that when the classifier predicts the positive class, that prediction is frequently correct. Therefore, the third task maps to C .

R² is not used for any of these tasks. R², or the coefficient of determination, is fundamentally a regression metric used to measure how much variation in a numerical target is explained by a regression model.

The verified mapping is therefore:

Machine translation using N-gram overlap → B. BLEU

Semantic similarity → A. BERTScore

Positive-class prediction quality → C. Precision

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