Assessment of Computer-Aided Translation Quality Based on Large-Scale Corpora

Abstract

CAT technology, utilizing translation memory and quality control tools, boosts translation efficiency and consistency. Yet, it faces challenges with cultural nuances, context, and creativity, requiring human intervention. This study explores leveraging large-scale corpora to enhance machine translation quality assessment accuracy and efficiency. Automated evaluation models surpass manual limitations, enhancing translation system performance and decision-making. Supervised and unsupervised machine translation quality assessment methods, coupled with data augmentation strategies, reduce model bias by reinforcing the connection between source and target languages. The proposed model achieves 94.8% and 92.6% correctness on Google Translate and Wikipedia datasets, slightly surpassing manual rates. Data augmentation and cascade evaluation notably improve translation evaluation accuracy, especially with large-scale corpora. Future research targets higher-quality pseudo-labeled data, refined evaluation models, and advanced assessment methods for diverse translation needs.