Research on few-shot learning methods for English technical translation in emerging scientific fields

A dual-encoder meta-learning framework is developed to address the challenge of English technical translation in emerging scientific fields under few-shot conditions. The system integrates a source encoder and a domain context encoder, both constructed with stacked transformer layers and enhanced by hybrid attention modules to capture domain-specific terminology and complex syntactic patterns. Domain-adaptive loss functions are incorporated to dynamically emphasize the accurate translation of high-priority scientific terms. Episodic meta-training enables the model to efficiently adapt its parameters for new scientific subdomains using minimal parallel data. Experimental evaluation is conducted on parallel corpora from nanotechnology and quantum computing, where annotated resources are limited and terminological evolution is rapid. The proposed approach demonstrates marked improvements in semantic preservation, technical term accuracy, and data efficiency compared to transformer-based and conventional meta-learning baselines. Ablation studies confirm the essential roles of task-adaptive initialization and domain-gated attention in achieving robust adaptation and stable performance across diverse scientific domains. These results establish the technical foundation for deploying scalable, data-efficient translation systems that meet the unique demands of knowledge-intensive, low-resource scientific environments.