Personalized AI-Based Translation Tools Enhanced for Accurate, Context-Aware Multilingual Text Processing
Aakansha Soy, Anjali Goswami · Procedia Computer Science · 2026
AI-enabled multilingual machine translation facilitates cross-lingual communication more easily and efficiently. While there has been development, most translation software still can’t adapt to customers’ needs, pick up cultural differences, or maintain context across fields. Due to the growing need for accurate multilingual communication in international relations, healthcare, education, and commerce, specialized, context-aware solutions are required. Most neural machine translation (NMT) systems are trained on generic corpora and produce literal translations without considering user preferences, discourse coherence, or colloquial idioms. They are less useful in practical contexts that require precision and individualization due to this difference. To solve this, the Personalized AI-based Contextual Translation (PACT) system uses user input, context-aware natural language understanding (NLU), reinforcement learning, and semantic embeddings. PACT enables accurate and fluid translations of multilingual materials by considering cultural traditions, domain-specific terminology, and user histories. Experimental results on multilingual benchmark datasets demonstrate that PACT outperforms baseline NMT models in terms of BLEU, METEOR, and BERTScore, while also reducing context-related errors. Large-scale technical and non-technical user research indicates improved personalization and translation relevance. Ultimately, PACT represents a significant step toward developing smart, human-centered translation systems that can effectively reduce language barriers and foster culturally aware, contextually accurate international communication. Future research will add multimodal translation and bias reduction.