Automated Translation Quality Evaluation Framework

For a sports media organization running a production translation service across multiple languages and sports domains, I designed and implemented a comprehensive evaluation framework using COMET-22 and XCOMET-XXL metrics. The system enables data-driven optimization through automated quality assessment, A/B testing of algorithm variations, and continuous production monitoring without requiring human reference translations.

The Challenge

The organization had deployed an improved translation system with OpenSearch-based few-shot selection and sophisticated glossary matching, but lacked systematic quality measurement:

• No automated metrics beyond manual review - expensive and inconsistent
• Difficult to quantify impact of algorithm improvements (e.g., MMR vs top-k selection)
• No baseline measurements for comparison when making changes
• Couldn’t evaluate production translations without human reference translations
• No framework for A/B testing different glossary matching strategies or few-shot selection parameters
• Quality issues discovered reactively through user complaints rather than proactive monitoring

They needed:

• Automated quality metrics enabling objective comparison of system variants
• Reference-free evaluation for production monitoring where human references unavailable
• Baseline establishment for measuring improvement over legacy system
• Testing framework for data-driven optimization
• Continuous quality tracking across language pairs and sports domains

Evaluation Metrics System

I designed and implemented a comprehensive evaluation framework combining reference-based and reference-free quality assessment:

Automated Metric Computation:

• COMET-22 for reference-based quality assessment when human reference translations available
• XCOMET-XXL for reference-free and reference-based quality estimation using only source and translation
• Automated pipeline computing metrics on validation sets across all language pairs
• Batch processing for efficient metric calculation across large test sets
• Parallel computation across language pairs for faster evaluation cycles

Reference-Based Evaluation (COMET-22 and XCOMET-XXL):

• Neural metric trained on human quality judgments, correlates highly with human assessment
• Evaluates translation quality by comparing source, translation, and human reference
• Used for validation sets with curated human reference translations
• Establishes gold-standard quality measurements for algorithm development
• Enables precise quantification of improvements from algorithm changes

Reference-Free Evaluation (XCOMET-XXL):

• Quality estimation without requiring human reference translations
• Analyzes source text and translation to predict quality score
• Critical for production monitoring where references unavailable
• Enables continuous quality tracking on live translation traffic
• Identifies quality degradation before user complaints

Evaluation Strategy

Baseline Establishment:

• Computed metrics on test sets using legacy system (random few-shot selection, static glossaries)
• Established baseline COMET-22 and XCOMET-XXL scores across language pairs
• Identified language pairs with lowest quality for prioritized optimization
• Created reference point for measuring all subsequent improvements

Evaluation Framework:

• Few-shot selection variants: Top-k similarity vs MMR diversity optimization
• Glossary matching variants: Different threshold configurations for fuzzy/semantic tiers
• Embedding model variants: Different SageMaker models with varying dimensions
• Controlled experiments on same test sets with statistical significance testing
• Quantified impact of each algorithmic choice on translation quality

Comparative Analysis:

• Measured quality improvement from CSV/JSON to OpenSearch migration
• Quantified MMR diversity optimization impact vs pure similarity ranking
• Validated three-tier glossary matching
• Compared different lambda values for MMR (tested 0.5, 0.7, 0.9)
• Established data-driven parameter selection for production deployment

Domain & Language Analysis:

• Segmented metrics by sports domain (football, basketball, tennis, etc.)
• Identified domains where terminology matching most critical
• Tracked quality across language pairs to find outliers requiring attention
• Guided few-shot example curation efforts toward underperforming segments

Quality Monitoring & Feedback Loop

Production Quality Tracking:

• XCOMET-XXL computed on sample of production translations for continuous monitoring
• Quality reports aggregated across language pairs and domains
• Alerting on quality degradation below established thresholds
• Trend analysis identifying gradual quality shifts over time

Iterative Improvement Cycle:

• Evaluation metrics inform few-shot example curation priorities
• Low-quality language pairs trigger targeted example collection efforts
• Glossary expansion guided by semantic matching tier analysis
• Continuous refinement based on measured impact on quality metrics

Impact Measurement:

• Demonstrated measurable quality improvement after OpenSearch migration
• Proved MMR-based diversity optimization improves consistency
• Validated three-tier glossary matching effectiveness across different terminology types
• Established data-driven culture where algorithm changes require metric validation

Technical Implementation

Metric Computation Pipeline:

• Batch processing of test sets with configurable parallelism
• Handles multiple language pairs concurrently for faster evaluation
• Storing of computed metrics for historical comparison

Evaluation Infrastructure:

• GPU-accelerated metric computation for COMET-22 and XCOMET-XXL models
• Reproducible evaluation pipelines for audit trail
• Report generation

Results & Impact

• Established comprehensive evaluation framework enabling data-driven optimization of translation system
• Quantified quality improvements from algorithmic changes (OpenSearch migration, MMR optimization)
• Reference-free evaluation (XCOMET-XXL) enables production quality monitoring without expensive human references
• Continuous monitoring identifies quality issues proactively before user impact
• Evaluation-driven feedback loop guides few-shot curation and glossary expansion efforts
• Metrics demonstrate measurable quality improvement over baseline legacy system
• Framework supports ongoing optimization as new algorithms and models become available

Technologies

Python, COMET-22, XCOMET-XXL, Statistical Analysis, MLOps, GPU Compute