Enterprise buyers still see BLEU scores in RFPs, benchmarks, and vendor decks as a universal measure of “translation quality.” Yet BLEU was never designed to capture meaning, domain adequacy, or business impact; it measures string overlap with a reference, not whether a translation is usable in a legal contract, a medical report, or a support workflow. In modern enterprise MT, especially with neural and LLM-based systems, BLEU alone is not only insufficient but can also be actively misleading.

This article explains where BLEU fails, why learned metrics such as COMET correlate better with human judgment, and how a domain‑based evaluation strategy (legal, medical, technical, etc.) produces decisions that actually reduce risk and improve ROI.

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When BLEU Is Misleading: The Domain Shift Problem

BLEU (Bilingual Evaluation Understudy) is based on n‑gram precision and a brevity penalty: it counts how many tokens and token sequences in the MT output also appear in one or more human reference translations. As long as the test set and the training data come from the same distribution, BLEU can roughly track whether a system is improving. The problem is that enterprise usage rarely matches that laboratory assumption.​

In production, domain shift is the norm, not the exception. A model tuned and evaluated on general news may show strong BLEU scores there. Still, the same system can deteriorate sharply on clinical narratives, financial prospectuses, or user-generated content, even when BLEU on the in‑domain test set looks “good.” A buyer who only sees a single aggregate BLEU score on a generic test set has no visibility into this mismatch.​

Several structural issues make BLEU especially fragile under domain shift:

Surface‑form dependence: BLEU only rewards exact or near‑exact token sequences. Paraphrases, alternative but correct terminology, or more natural word order are penalized if they diverge from the reference, which is common when moving between domains or editorial styles.​

Reference and tokenization sensitivity: BLEU scores change significantly with different reference translations and tokenization schemes; comparing scores across datasets, domains, or vendors is often meaningless unless all conditions are tightly controlled.​

Lack of semantic understanding: BLEU does not model meaning or factual correctness. A translation can be terminologically wrong or even misleading, but still achieve high n‑gram overlap, especially if it mechanically reuses frequent phrases learned from an adjacent domain.​

Academic and industry studies have shown cases in which systems optimized to maximize BLEU produce lower human‑perceived quality than baselines, especially when quality differences are subtle or when test sets are small. For enterprise stakeholders, this translates into a dangerous situation: a vendor can show higher BLEU on a convenient test set without delivering better output on the customer’s real, domain‑specific content.​

In practice, the “domain shift problem” has two concrete consequences for enterprise MT:

A single BLEU number averaged across mixed content is not a reliable indicator of suitability for high‑risk domains (e.g., regulatory, medical, legal).

Optimizing systems and making buying decisions solely on BLEU encourages overfitting to narrow benchmarks rather than robust performance where it matters.

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Why COMET correlates better with human judgment in enterprise QA

To address BLEU’s limitations, the MT research community has developed learned evaluation metrics such as COMET (Crosslingual Optimized Metric for Evaluation of Translation). COMET uses neural encoders, trained on large datasets of human‑rated translations, to estimate quality at the sentence and system level in a way that more directly reflects human preferences.

Unlike BLEU, which operates on n‑gram counts, COMET embeds the source sentence, the MT hypothesis, and (optionally) one or more references into a continuous semantic space. It predicts a quality score learned from human assessments. This architecture gives it several advantages for enterprise scenarios:

Semantic sensitivity: COMET can distinguish outputs that preserve meaning and nuance from those that merely appear similar at the surface. This is critical when accurate rendering of obligations, contraindications, or technical specifications is more important than literal wording.

Better correlation with human ratings: Multiple shared tasks and research benchmarks have shown that COMET correlates substantially better with human quality judgments than traditional n‑gram metrics, especially for modern neural systems where differences are more subtle.​

Robustness to paraphrase and style: Because COMET reasons at the representation level, it is less likely to penalize acceptable paraphrases or stylistic choices that humans consider correct, a frequent requirement in enterprise localization workflows.

For enterprise QA teams, this translates into more actionable signals:

COMET scores track human judgments more closely across different domains and language pairs, making it easier to detect real quality improvements from model updates, fine‑tuning, or prompt changes.

Sentence‑level COMET scores can be used to drive selective human review (e.g., send only low‑COMET segments to linguists), enabling cost‑effective hybrid workflows where human effort is focused where risk is highest.

Importantly, COMET is not a silver bullet. No automatic metric can completely replace expert human evaluation in safety‑critical or regulatory contexts. However, in combination with targeted human reviews, COMET provides a far more reliable foundation than BLEU for continuous quality monitoring and vendor/model comparison in an enterprise setting.

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How we run Machine Translation evaluation by domain (legal, medical, Technical)

From an enterprise perspective, the central question is not “What is the overall BLEU/COMET score?” but “Is this system trustworthy for my domain, my risk profile, and my workflows?” Answering that question requires domain‑specific evaluation, not one global number.

A robust evaluation framework for Pangeanic‑style deployments typically includes the following steps:

Define domain‑specific test sets

Curate representative parallel or pseudo‑parallel corpora for each critical domain: legal (contracts, terms and conditions, regulatory correspondence), medical (patient information, clinical content, device instructions), technical (manuals, specifications, support knowledge bases), and others as needed.

Ensure that test sets are disjoint from training and adaptation data to avoid inflated scores. 1.

Run multi‑metric automatic evaluation (BLEU + COMET, not BLEU alone)

Compute BLEU for backward compatibility with legacy reporting and to maintain continuity with past benchmarks.

Compute COMET (and, where appropriate, complementary metrics such as TER or chrF) to capture semantic adequacy and fluency more faithfully.

Evaluate separately per domain and language pair, not just on a pooled corpus, to surface domain‑specific weaknesses. 1.

Sample‑based human evaluation aligned with business risk

For each domain, draw stratified samples across COMET score ranges (e.g., low, medium, high) and have professional linguists or subject‑matter experts rate translations on adequacy, fluency, and criticality of errors.

Use these human ratings to validate that COMET thresholds correspond to acceptable quality levels in context (for example, “segments below X should never be published without review”). 1.

Error categorization and feedback loop

Classify errors by type (terminology, omissions, additions, mistranslations, formatting, register) and by severity (minor, major, critical).

Feed error patterns back into model adaptation: glossary reinforcement, domain‑specific fine‑tuning, prompt or decoding adjustments, and, where relevant, hybrid MT + LLM post‑editing strategies. 1.

Ongoing monitoring and regression control

Integrate COMET‑based scoring into continuous delivery pipelines so that the domain automatically evaluates new model versions or configuration changes before deployment.

Set domain‑specific guardrails (e.g., “no release if COMET drops by more than Δ on legal French–Spanish”) to prevent silent quality regressions that BLEU might miss.

In high‑risk domains (legal disclosures, medical content, financial reporting) this framework is complemented with human‑in‑the‑loop review policies and, increasingly, with task‑specific small models or constrained workflows that further reduce the likelihood of critical errors.

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Used this way, BLEU becomes a legacy compatibility metric, useful for trend lines and historical comparisons, but no longer the primary decision driver. COMET, paired with domain‑specific test sets and targeted human evaluation, provides the level of nuance and reliability that enterprise MT evaluation now requires.

Here is a focused FAQ set tailored to the article as published; you can mirror the wording in the on-page FAQ block and in structured data.

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Frequently Asked Questions (FAQ)

Q1. Why is BLEU not enough to evaluate enterprise machine translation?

BLEU measures n‑gram overlap with reference translations, so it is highly sensitive to surface form and test-set choice and does not directly capture meaning, domain adequacy, or business risk.​

Q2. What is COMET in machine translation evaluation?

COMET is a learned evaluation metric that uses neural representations of the source, hypothesis, and reference to predict a quality score trained on human judgments, making it better aligned with how linguists perceive adequacy and fluency.​

Q3. How does COMET correlate with human judgment compared to BLEU?

Studies consistently show that COMET exhibits substantially higher correlation with human ratings than BLEU, particularly for modern neural MT systems where quality differences are subtle and domain‑dependent.​

Q4. Why is domain-specific evaluation (legal, medical, technical) necessary?

Different domains have distinct terminology, risk profiles, and acceptable error types, so a system that scores well on generic news can still fail on contracts, clinical content, or technical manuals unless it is evaluated on in‑domain test sets.​

Q5. How should enterprises combine automatic metrics and human review?

A robust framework uses COMET (and optionally BLEU/TER) for large-scale, repeatable scoring, then samples segments for expert human review in high‑risk domains to validate thresholds and guide model adaptation.l​

Q6. Can automatic MT metrics fully replace human evaluation?

No. Automatic metrics are invaluable for monitoring and model comparison, but human reviewers remain essential for safety‑critical, regulatory, and brand‑sensitive content where nuance, context, and liability must be carefully assessed.