Defining quality in MT and LLM translation workflows
Historically, the quality of machine translation was often measured with an academic yardstick: word-for-word similarity to a human reference. Metrics like BLEU (Bilingual Evaluation Understudy), based on n-gram precision, provided a standardized score. However, these legacy metrics are no longer sufficient. Models now produce highly fluent and contextually varied text. An LLM can generate a translation that is semantically accurate and stylistically different from the reference text, yet still receive a poor BLEU score simply because it used different words. This reveals a fundamental flaw: measuring lexical overlap is not the same as measuring quality.
This shift necessitates a focus on Human-AI symbiosis. The goal is not merely to generate text but to empower professional linguists to work faster and better. Technologies like Lara, Translated’s translation AI, are designed to support professional workflows where humans refine output to a target standard. Lara focuses on full-document context to help maintain consistency and support nuance across the entire file, not just sentence by sentence.
This has led to the rise of modern automated metrics that better correlate with human perception. Neural network-based models like COMET and BLEURT are trained on human judgments, allowing them to assess semantic similarity rather than just lexical matching. Concurrently, “LLM-as-a-Judge” approaches use large models to evaluate outputs and can correlate with human judgments in some settings, though reliability varies by task and language.
Yet, even these advanced scores are only half the story. An automated metric, no matter how sophisticated, provides a score in a vacuum. It cannot tell you how a translation will perform in a real-world business workflow. It does not measure brand alignment, terminology compliance, or, most critically, the impact on human productivity. A quality score is a technical indicator. What an enterprise needs is a framework that connects that score to tangible business outcomes like cost, speed, and ROI.
Key metrics for evaluating MT and GenAI translation performance
To bridge the gap between a technical score and a business outcome, evaluation must shift from academic metrics to business-centric KPIs. This requires a multi-layered approach that captures not just the linguistic quality of a translation, but its operational and strategic value.
Time to Edit (TTE): The gold standard for measuring efficiency
One of the most practical metrics for evaluating enterprise-grade AI translation in production is Time to Edit (TTE).
TTE measures the real-world editing effort required for professional linguists to bring AI-generated translation to the desired quality level, often expressed as time normalized by word count.
Unlike automated scores that measure a translation against a static reference, TTE measures its impact on the most valuable resource in the localization workflow: the human expert. A lower TTE is typically associated with higher productivity and can support faster turnaround and lower costs, depending on workflow design and review requirements. It is a central KPI for a localization program because it moves the discussion from “How good is the machine?” to “How much more effective does the machine make our team?” This provides a direct operational signal that can be used to quantify ROI and serves as a primary indicator of progress toward translation singularity.
Errors Per Thousand (EPT): Benchmarking accuracy
While TTE measures efficiency, accuracy must be tracked with precision. Errors Per Thousand (EPT) is a quality metric showing the number of errors identified per 1,000 translated words in a linguistic QA process.
This metric can be useful for benchmarking translation accuracy and identifying specific improvement areas within the model or the terminology database. By tracking EPT alongside TTE, organizations gain a more rounded view of performance. TTE tells you how fast the work is being done, while EPT ensures that the speed does not come at the cost of linguistic precision.
Error analysis (MQM framework)
To improve these metrics, you need to understand the root cause of the issues. The Multidimensional Quality Metrics (MQM) framework provides a structured methodology for categorizing errors, such as accuracy, fluency, terminology, and style. This data is invaluable for the human-in-the-loop feedback cycle, providing insights that can inform targeted improvements to terminology resources, prompts, training data, and review workflows.
Task-based evaluation
Ultimately, a translation’s quality is determined by its ability to achieve a specific business goal. A task-based evaluation assesses the translation’s fitness for purpose. For an e-commerce site, this might be measuring the conversion rate of a translated product page. For customer support, it could be the resolution rate of tickets handled with translated content. This approach connects the linguistic output directly to a strategic business outcome, adding an additional layer of validation for an AI translation solution.
Proven frameworks for consistent quality measurement
A robust evaluation framework is not about finding a single, perfect metric. It is about using the right metric for the right job. A sophisticated, data-driven organization implements a hybrid evaluation model that combines the scalability of automated metrics with the business insight of human-centric measures.
The hybrid evaluation model in practice
This model assigns distinct roles to different types of metrics within the localization workflow:
- Role 1: Continuous quality monitoring with automated metrics. Modern neural metrics like COMET should be integrated directly into the development and localization lifecycle. Their primary role is to act as a rapid, automated quality gate. For instance, running a COMET evaluation as part of a CI/CD pipeline can catch significant quality regressions in the MT engine before they ever reach a human translator. This is a scalable way to monitor baseline quality.
- Role 2: Strategic decision-making with TTE. TTE is the metric for making critical business decisions. Because it directly measures productivity and cost, it is the definitive metric for strategic planning:
- Vendor selection: Conducting a controlled A/B test between two or more MT providers and measuring the TTE for each is one of the most reliable ways to determine which solution will deliver the best ROI.
- Calculating ROI: By tracking TTE over time, you can directly quantify the cost savings and efficiency gains from your AI translation program.
- Internal benchmarking: Use TTE to measure the performance of different, fine-tuned MT models to see which is most effective for your specific content.
Benchmarking performance: How to run a fair A/B test
To accurately benchmark different MT solutions, a controlled environment is essential. An effective A/B test requires:
- Identical content: The exact same set of source documents must be translated by each MT engine.
- Consistent human resources: The same group of professional linguists must perform the post-editing for all outputs to eliminate variability in editing speed and style.
- A centralized platform: The test should be conducted within a single translation platform that can accurately and automatically measure the TTE for each segment.
By isolating the MT engine as the only variable, you can generate clean, reliable TTE data that provide a clearer, more comparable view of each solution’s performance for your content and reviewers.
Implementing automated evaluation and human-in-the-loop review
A framework is only as valuable as your ability to implement it. Measuring metrics like TTE at scale and creating a data-centric feedback loop is impossible without the right technology platform.
The role of a modern translation platform in quality evaluation
A modern localization platform is the engine of a data-driven quality program. Platforms like TranslationOS are designed not just for project management, but as a data platform for performance optimization. A modern translation platform can capture editing-time signals needed to calculate TTE at scale, and TranslationOS centralizes workflows and performance insights for enterprise localization.
This creates a powerful data-centric foundation. The feedback from human editors is not lost. Instead, it becomes structured feedback that can support routing decisions, quality monitoring, and continuous improvement over time. This is the essence of a human-in-the-loop workflow. The AI model, such as Translated’s adaptive MT or Lara, learns from the human experts. This symbiotic relationship leads to progressively better performance and a consistently decreasing TTE over time.
Real-world impact: Scaling global reach with quality
The value of this quality-first framework is best demonstrated by global leaders who prioritize localization strategy. Airbnb, for example, worked with Translated on a large-scale language expansion program spanning 80+ locales, including 30+ completely new languages, delivered in a condensed timeframe
This expansion required more than translation speed and benefited from workflows designed to support consistency and brand voice across locales. By implementing a robust framework that balances automation with human expertise, Airbnb turned localization into a driver for international growth. This success illustrates that when quality is measured and managed effectively, it becomes a competitive advantage rather than a cost center.
Future standards shaping MT and LLM translation assessment
The industry is moving toward a more holistic and business-aware understanding of translation quality. Future evaluation standards will be multi-dimensional, combining scores for linguistic accuracy, task-specific effectiveness, and, crucially, workflow efficiency. As AI models become more sophisticated, interest in explainable AI (XAI) is likely to grow with systems that can justify their translation choices to enhance trust and transparency for human reviewers.
However, businesses do not need to wait for these future standards to mature. The future of translation quality measurement is not just about developing better machines. It is about implementing better, more intelligent measurement practices today.
Adopting a business-centric evaluation framework, centered on efficiency and powered by metrics like TTE and EPT, is the critical first step. It allows you to cut through the noise of competing quality claims and focus on what truly matters, which is the measurable impact on your localization workflow. By shifting the conversation from abstract scores to tangible ROI, de-risk your investment and better realize the strategic potential of AI in your global content strategy.