Automated evaluations

In the context of language technology and AI, automated evaluations provide a rapid, scalable way to assess performance. Unlike human evaluation, which is nuanced but slow and expensive, automated metrics offer immediate feedback. In machine translation, algorithms such as BLEU (Bilingual Evaluation Understudy), TER (Translation Edit Rate) and COMET compare the AI-generated text against a "gold standard" human translation. They calculate similarity based on word overlap, sentence structure and semantic closeness.

These tools allow developers and data scientists to process large datasets efficiently, identifying patterns in translation errors, terminology usage or structural consistency. They are essential for benchmarking: determining if a new model version performs better than the last. However, automated evaluations are rarely used in isolation for critical content. Because they rely on mathematical proximity rather than true understanding, they can miss subtle errors in tone or cultural appropriateness. Therefore, best practice involves using automated scores as a first-pass filter or performance tracker, complemented by human-in-the-loop review for final quality assurance.