neuroverkkokäännöstä

Neuroverkkokäännös, often referred to as neural machine translation (NMT), is a modern approach to machine translation that uses artificial neural networks to predict the likelihood of a sequence of words, typically making it one of the most advanced machine translation techniques. Unlike older statistical machine translation methods that relied on breaking down sentences into smaller pieces and translating them independently, NMT models consider the entire sentence as a single unit.

The core of NMT is a deep neural network, usually an encoder-decoder architecture. The encoder processes the source language sentence and compresses its meaning into a vector representation. The decoder then takes this vector and generates the translated sentence in the target language, word by word. This allows the model to capture long-range dependencies and context within the sentence, leading to more fluent and accurate translations.

NMT has significantly improved translation quality across many language pairs compared to previous methods. It is widely used by major technology companies in their translation services and continues to be an active area of research and development, with ongoing efforts to improve its performance, handle low-resource languages, and reduce computational costs.