Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian

TL;DR

This study introduces a detailed, statistically rigorous evaluation method for machine translation quality, focusing on English to Croatian, revealing neural MT's superior performance especially in complex linguistic phenomena.

Contribution

It develops a tailored MQM error taxonomy for Slavic languages and applies statistical analysis to compare different MT paradigms, highlighting neural MT's advantages.

Findings

Neural MT reduces errors by 54% compared to phrase-based systems.

Neural MT excels in long-distance agreement errors.

The tailored MQM taxonomy improves annotation accuracy.

Abstract

This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to-Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54\%). Moreover, we conducted an additional analysis of agreement errors in which we distinguished between short (phrase-level) and long distance (sentence-level) errors. We discovered that phrase-based MT approaches are of limited use for long distance agreement phenomena, for which neural MT was found to be especially effective.