Machine Translation
Definition
AI systems that automatically translate text or speech from one natural language to another.
Machine translation (MT) has evolved through three paradigms: rule-based (1950s-2000s), statistical (2000s-2016), and neural (2016-present). The neural era began with sequence-to-sequence models and attention mechanisms, and was transformed by the transformer architecture. Google Translate, DeepL, and Meta's NLLB (No Language Left Behind) now support over 200 languages. Modern approaches include both dedicated translation models and general-purpose LLMs that perform translation as one of many capabilities. Key challenges include low-resource languages (those with little training data), preserving tone and style, handling idiomatic expressions, and domain-specific terminology. Quality has improved dramatically — for high-resource language pairs like English-French, neural MT approaches near-human quality for many text types.
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