What metrics are typically used to evaluate the quality of machine translation output from a Transformer model?

Several metrics are typically used to evaluate the quality of machine translation output from a Transformer model, with BLEU (Bilingual Evaluation Understudy) being the most common. BLEU measures the similarity between the machine-translated output and one or more reference translations. It calculates the precision of n-grams (sequences of n words) in the machine translation output compared to the reference translations. BLEU also incorporates a brevity penalty to penalize translations that are too short. A higher BLEU score indicates better translation quality. However, BLEU has some limitations. It primarily focuses on precision and does not explicitly measure recall. It also struggles with capturing semantic meaning and can be sensitive to small variations in wording. Another commonly used metric is METEOR (Metric for Evaluation of Translation with Explicit Ordering). METEOR addresses some of the limitations of BLEU by incorporating recall, synonym matching, and stemming. It also uses a more sophisticated alignment algorithm to match words and phrases between the machine translation output and the reference translations. This makes METEOR more robust to variations in wording and better at capturing semantic meaning. TER (Translation Edit Rate) measures the number of edits (insertions, deletions, substitutions, and shifts) required to transform the machine translation output into the reference translation. A lower TER score indicates better translation quality. TER is more intuitive than BLEU and METEOR, as it directly measures the amount of effort required to correct the machine translation output. However, TER can be sensitive to the choice of edit operations and may not always accurately reflect the perceived quality of the translation. ChrF (Character n-gram F-score) evaluates translation quality based on character n-gram overlap. It calculates precision and recall of character n-grams between the machine translation output and the reference translations, then combines them into an F-score. This is particularly useful for morphologically rich languages where subword tokenization or character-level representations are common. Finally, human evaluation remains the gold standard for evaluating machine translation quality. Human evaluators are asked to rate the fluency and adequacy of the machine translation output. Fluency measures how natural and grammatically correct the translation is, while adequacy measures how well the translation conveys the meaning of the source text. While human evaluation is more reliable than automatic metrics, it is also more expensive and time-consuming. Therefore, automatic metrics are typically used for large-scale evaluation and comparison of machine translation systems, while human evaluation is reserved for evaluating the final performance of the best systems.