MACHINE TRANSLATION AND LINGUISTIC CHALLENGES IN NEURAL TRANSLATION SYSTEMS
Keywords:
Neural Machine Translation, Machine Translation, Linguistic Challenges, Semantic Ambiguity, Syntax Differences, Pragmatics, Idiomatic Expressions, Deep Learning, Contextual Understanding, Multilingual Models.Abstract
This thesis examines the main linguistic challenges faced by neural machine translation systems, including semantic ambiguity, syntactic differences, and cultural context. It explores current solutions such as context-aware models, linguistic knowledge integration, and human-machine collaboration. The study highlights the strengths and limitations of modern translation technologies and suggests directions for future improvements to enhance translation accuracy and naturalness.
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References
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