The article discusses the challenges in evaluating machine translation quality and introduces optimal reference translations as a solution. It explores the creation process, annotation campaign, and statistical analysis to enhance translation evaluation methodologies.
The authors highlight the importance of context in evaluating translations and provide insights into annotator differences and their impact on evaluation outcomes. The study emphasizes the significance of segment-level ratings in predicting document-level scores and offers valuable recommendations for future translation evaluations.
Key points include proposing optimal reference translations, conducting an annotation campaign with diverse annotators, analyzing inter-annotator agreement, modeling overall quality from components, and examining differences in annotator approaches. The article underscores the need for context-aware evaluation methods to improve translation quality assessments.
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