Deep learning-based automatic evaluation model for translation quality of master of translation and interpreting

Abstract

Translation quality assessment functions as a vital component which Master of Translation and Interpreting programs use to evaluate students’ language skills and their translation abilities. Conventional human-based translation scoring is highly subjective, labour-intensive, and lacking in reliability. To overcome these limitations, the research offers a Lotus Effect-Attention-based Bi-directional Gated Recurrent Unit (LE-Att-Bi-GRU) deep learning (DL) model for automatic translation quality assessment. The Translation Quality Evaluation dataset incorporates large parallel corpora involving of MTI student translations, expert reference translations, and consistent bilingual datasets. Collected text undergoes pre-processing steps such as text normalization and sentence alignment, which are used to ensure data standardisation and accuracy. Feature extraction adopts Term Frequency-Inverse Document Frequency (TF-IDF) to quantify linguistic features such as terminology accuracy, lexical richness, and structural coherence, and Sentence-Bidirectional Encoder Representations from Transformers (Sentence-BERT) embeddings to capture semantic equivalence between source and translated texts. The proposed LE-Att-Bi-GRU model improves semantic representation by incorporating a lotus-inspired division method that decreases noise and focuses essential semantic cues. The Bi-GRU captures bidirectional contextual dependencies, while the integrated attention module dynamically assigns weight to essential translation segments, developing translation accuracy, and style evaluation. The supervised model is trained using expert-annotated quality scores as ground truth. Experimental outcomes reveal high correlation with professional assessment standards and superior performance compared to precision (95.1%), accuracy (92.3%), F-measure (94.2%), AUC (0.926), recall (93.4%), Pearson (0.842), and Spearman (0.829). This research contributes to automatic, scalable, and pedagogically meaningful evaluation for MTI training, curriculum development, and future AI-assisted translation evaluation systems. Graphical abstract show the Overview of the proposed LE-Att-Bi-GRU framework for automatic MTI translation quality evaluation using semantic and linguistic features.