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Machine translation plays a critical role in expanding access to information across diverse languages and cultures. For children's literature, there is a need for translation models that can preserve both linguistic accuracy and emotional sensitivity. However, existing automated systems often struggle with the adaptations required for young readers. This study addresses this gap by developing a novel English-to-Tamil translation model for children's stories, combining the Universal Networking Language (UNL) for semantic representation with emotional paraphrasing techniques. Our approach uses a neuro-symbolic AI framework, specifically integrating the T5 transformer and few-shot learning, allowing effective model adaptation with minimal data. Evaluation with BiLingual Evaluation Understudy (BLEU), Translation Error Rate (TER), and Metric for Evaluation of Translation with Explicit Ordering (METEOR) scores (0.8978, 0.15, and 0.8869 respectively) highlights the model's high performance in maintaining both accuracy and contextual sensitivity. These metrics underscore the system's capability to deliver culturally relevant and child-appropriate translations. This research contributes to machine translation by bridging neural and symbolic methods, providing an adaptable, low-resource solution that supports cross-cultural understanding and accessible content for young readers.
Matan et al. (Mon,) studied this question.