A hybrid intelligent assessment model for English translation education with improved BERT and SVM

Abstract

Assessing student translations in real classrooms still leans heavily on human judgment, which can vary across raters, is time-consuming, and scales poorly. With the rapid advancement of natural language processing (NLP), automatic assessment models have shown potential to enhance objectivity and consistency in translation evaluation. This paper proposes BERT-SVM EduScore, a hybrid assessment model designed for English translation education. The model couples a BERT-base encoder adapted with domain-/task-specific training and a contrastive objective with compact linguistic and alignment features, and feeds the resulting representations into margin-based Support Vector Machines (SVM) heads with monotonic calibration to produce holistic and rubric-aligned sub-scores. We conduct experiments on the English–Chinese portion of the MLQE-PE machine-translation quality estimation dataset, which we use as a proxy for sentence-level scoring in classroom-like settings. BERT-SVM EduScore is compared against string-based metrics (BLEU, METEOR, TER, chrF++), embedding-based semantic metrics (YiSi-1, MoverScore, BERTScore), and learned evaluators (BLEURT, COMET, PRISM, BARTScore, TransQuest) using Quadratic Weighted Kappa (QWK), mean absolute error (MAE), Pearson’s r, and runtime. On MLQE-PE it achieves QWK 0.76, MAE 0.12, and r 0.84, improving QWK over COMET by + 0.08 and over TransQuest by + 0.05 while running at about 22.5 ms per sentence (≈ 44 sentences per second) on commodity GPU hardware. Ablation studies show that domain-adaptive pretraining yields the largest gains, contrastive learning provides additional improvements, and metric distillation contributes smaller but consistent benefits. These quantitative results suggest that the proposed model can serve as a technically feasible component for translation education, offering calibrated scores and lightweight diagnostic signals under realistic latency constraints; validation on real course assignments and with teachers and students in the loop is left for future work.