Neural Network-based Machine Translation for a Low-Resource Language
Keywords:
Neural Machine Translation (NMT), Low-Resource Languages (LRLs), Deep Learning, Multilingual NMT (MNMT)Abstract
Neural Machine Translation (NMT) has improved translation quality for high-resource languages. However, low-resource languages (LRLs) with limited text data pose unique challenges due to data scarcity, morphological complexity, and lack of linguistic resources. Many languages, such as Hindi, Sanskrit, Marathi, Pali, Urdu have complex grammatical structures but insufficient bilingual corpora for training modern AI models.
This paper explores how neural networks and deep learning techniques overcome these challenges. We examine transfer learning, multilingual NMT, unsupervised learning, and large-scale AI models such as GPT-4, M2M-100, Google Translator, and NLLB to enhance translation quality for LRLs. Additionally, we integrate findings from recent research on character-level encoding, self-supervised learning, and back-translation. Finally, we discuss future directions in AI-driven translation, including multimodal translation, community-driven data collection, and ethical considerations in LRL translation.
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