Development and integration of fusion processing algorithms for heterogeneous text data in Japanese translation systems

Abstract

A novel fusion-based Japanese-English translation system is presented to address the persistent challenges posed by heterogeneous, multi-domain text data in practical engineering environments. The study proposes an algorithmic framework designed to efficiently handle formal, informal, and technical language forms. It uses rule-based symbolic processing and advanced neural network translation models. The method aims to prepare complex Japanese input translation thru strict multistage data normalization, highly featured word segmentation, and adaptive contextual annotation. The core dual-path architecture dynamically controls the contribution of symbolic and neural components thru a context-aware weighting mechanism. This helps optimize translation fidelity based on the language characteristics and domain relevance of the input text. According to a comprehensive empirical evaluation, the system performs well in high variability domains and rare/ambiguous language patterns, and outperforms traditional neural network and symbolic baseline models in terms of efficiency and accuracy. Quantitative results show that the BLEU's appropriateness indicators, scores, and human fluency have been greatly improved. The technical design ensures industrial-grade throughput and scalability thru parallel reasoning and optimization. By combining symbolic language features and neural representation learning, the framework provides a reliable and adaptable solution to the limitations of current machine translation, and lays a technical foundation for advanced multilingual applications.