AI-driven translation utilizes neural machine translation (NMT) models, such as Transformer-based architectures, to process and generate multilingual text. These models employ deep learning techniques, analyzing syntactic and semantic structures to produce accurate translations.
Key processes include:
Tokenization: Breaking text into smaller linguistic units for analysis.
Embedding Representation: Converting words into vector representations for contextual understanding.
Contextual Encoding: Capturing sentence dependencies using attention mechanisms.
Decoding: Generating translated output with grammatical accuracy.
