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Sentence-level Quality estimation (QE) of machine translation istraditionally formulated as a regression task, and the performance of QE modelsis typically measured by Pearson correlation with human labels. Recent QEmodels have achieved previously-unseen levels of correlation with humanjudgments, but they rely on large multilingual contextualized language modelsthat are computationally expensive and make them infeasible for real-worldapplications. In this work, we evaluate several model compression techniquesfor QE and find that, despite their popularity in other NLP tasks, they lead topoor performance in this regression setting. We observe that a full modelparameterization is required to achieve SoTA results in a regression task.However, we argue that the level of expressiveness of a model in a continuousrange is unnecessary given the downstream applications of QE, and show thatreframing QE as a classification problem and evaluating QE models usingclassification metrics would better reflect their actual performance inreal-world applications.

Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications