Enhancing Domain-Specific English-Chinese Neural Machine Translation with Data Augmentation and Term Adaptation Techniques

Neural Machine Translation (NMT) has achieved remarkable progress in natural language processing. However, developing domain-specific translation models with limited data remains a critical challenge, as NMT heavily relies on largescale datasets. This paper presents an English-Chinese NMT engine designed to address these challenges by leveraging domain adaptation, data augmentation, and innovative term translation techniques. The engine incorporates a placeholderbased fuzzy matching method and a model learning guidance strategy to enhance translation quality. Experimental results demonstrate that the proposed engine significantly outperforms commercial systems in translating domain-specific content, particularly in the social media context. This work contributes to improving NMT for low-resource domains and highlights strategies for creating effective, adaptable translation models for specialized applications.