Many automatic evaluation metrics for machine translation (MT) rely on making comparisons to human translations, a resource that may not always be available. We present a method for developing sentence-level MT evaluation metrics that do not directly rely on human reference translations. Our metrics are developed using regression learning and are based on a set of weaker indicators of fluency and adequacy (pseudo references). Experimental results suggest that they rival standard reference-based metrics in terms of correlations with human judgments on new test instances.
Using the regression learning and a set of indicators of fluency and adequacy as pseudoreferences, Albrecht and Hwa REF present an unsupervised MT evaluation work at sentence level performance.
Regression for Sentence-Level MT Evaluation with Pseudo References
45th Annual Meeting of the Association of Computational Linguistics
Computer Science