A classical text translation method based on real-time neural post-editing
This paper presents a technical solution for the translation of classical texts by integrating a deep learning-based preliminary translation model with a real-time neural post-editing framework. The system uses an advanced Transformer architecture for initial translation and a hierarchical attention mechanism to optimize translation output in real time. Data preprocessing techniques for classical corpora ensure the preservation of rare lexical items and syntactic complexity. Experimental evaluation was performed using a high-performance computing environment, and the fidelity and running efficiency of the translation were evaluated. It outperforms the transformer-only and traditional rule-based baselines on BLEU and Translation Edit Rate metrics. Experimental evaluation is performed on a large-scale, curated parallel dataset of historical texts. The analysis results show that the inference latency is reduced, the semantic alignment is enhanced, and the domain-specific expressions can be effectively handled. The neural post-editing module can correct surface and deep contextual errors, thus generating translation results that are very close to expert human translation. This proves the effectiveness of the system in large-scale, high-precision translation tasks in the fields of computational linguistics and digital humanities. The study also pointed out the technical problems in the current industry adaptation and resource utilization, and emphasized the necessity of further improvement of the technology.