Statistical Machine Translation

Statistical Machine Translation (SMT) aims to automatically translate text between languages using statistical models to learn patterns from parallel corpora. Current research focuses on improving translation quality, particularly for low-resource languages, by exploring advanced architectures like transformers and incorporating techniques from reinforcement learning and preference optimization to refine model outputs. SMT remains a vital area of natural language processing, impacting fields like search engine optimization, cross-lingual information retrieval, and educational applications through advancements in both model performance and evaluation methodologies.