Neural Machine Translation for Low-Resource Indian Languages: Challenges and Future Directions
Keywords:
Neural Machine Translation, Low-Resource Languages, Transformer Models, Indian Linguistics, Multilingual NLP, Semantic Preservation.Abstract
Neural Machine Translation (NMT) has revolutionized cross-lingual communication, achieving near-human performance for high-resource languages. However, low-resource Indian languages, which encompass morphologically rich and syntactically diverse linguistic systems, remain underrepresented in existing neural models. This paper presents a theoretical analysis of challenges, limitations, and emerging solutions in NMT for low-resource Indian languages. By synthesizing recent research in transformer-based architectures, multilingual embeddings, transfer learning, and data augmentation techniques, we propose a conceptual framework for improving translation quality, semantic fidelity, and cultural preservation. The paper also outlines future research directions, including the integration of indigenous knowledge corpora, unsupervised learning paradigms, and hybrid neural-symbolic models, to enable scalable and contextually aware translation systems for India's linguistic diversity.
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