From scores to insights: Predicting MT errors using reliable metrics and linguistic typology in slavic languages

Abstract

Machine Translation (MT) evaluation plays a crucial role in advancing systems translating into morphologically rich, low-resource languages such as Slovak. Existing automatic evaluation methods typically offer a single quality score, lacking insight into specific error types. A novel linguistically informed methodology that predicts the probability of MT error categories by integrating manual annotation with automatic evaluation metrics is proposed. The method builds on a modified MQM framework adapted for Slovak and employs a dataset of English-to-Slovak translations, combining outputs from statistical and neural MT systems with human reference translations. Manual annotations identified five linguistically motivated error categories. Reliability of 68 automatic metrics was assessed using Cronbach’s alpha, correlation coefficients, coefficient of determination (R²), and entropy. Bootstrapped logistic regression models were then developed to predict error occurrence probabilities. The proposed methodology improves the explainability and reliability of automatic MT evaluation by bridging the gap between holistic scoring and detailed error categorization. It significantly reduces the human effort required for quality assessment while maintaining a high degree of linguistic relevance, particularly for complex target languages like Slovak.

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  Predicts probabilities of specific MT error categories
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  Integrates linguistic expertise with statistical reliability analysis
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  Reduces human effort in MT evaluation while preserving linguistic precision