ThandoluhleMoyo/Multi-Metric-NLP-Framework-for-Detecting-and-Classifying-Translation-Errors

Developed a scalable, automated framework to evaluate isiZulu machine translation quality using multi-metric NLP evaluation, Bayesian threshold optimization, and machine learning. The pipeline detects mistranslations, classifies error types, and enables data-driven improvement of low-resource language benchmarks. # Multi-Metric-NLP-Framework-for-Detecting-and-Classifying-Translation-Errors ## Executive Summary High-quality evaluation benchmarks are critical for developing reliable machine translation (MT) systems. However, for low-resource languages, reference translations in widely used benchmarks can contain inaccuracies that distort model evaluation and downstream decision-making. This project presents a scalable, automated validation framework for assessing translation quality in the isiZulu FLORES dataset. By combining lexical metrics, semantic similarity, Bayesian threshold optimization, and machine learning, the pipeline enables: - Automated detection of mistranslations - Classification of translation error types - Data-driven improvement of multilingual benchmarks The solution demonstrates how advanced NLP metrics and ML models can replace manual review processes while producing interpretable, business-relevant insights. ## Business Problem Organizations building or evaluating multilingual NLP systems face several challenges: 1. Evaluation Reliability Benchmark datasets for low-resource languages may contain incorrect or inconsistent reference translations, leading to misleading performance scores. 3. Scalability Constraints Human review is expensive, slow, subjective, and infeasible at scale—especially across many languages. 5. Decision Risk Poor-quality benchmarks can result in: - Overestimated model performance - Incorrect deployment decisions - Propagation of errors into downstream applications Without automated validation pipelines, organizations lack confidence in both their data assets and model evaluation outcomes. ## Solution Overview This project implements a multi-stage validation and classification pipeline to automatically assess translation quality and diagnose error causes. The pipeline: - Compares original vs corrected isiZulu translations - Computes multiple complementary NLP evaluation metrics - Learns optimal decision thresholds using Bayes …