Example Concept: Neural machine translation (NMT) uses encoder-decoder architectures with attention mechanisms. The encoder processes the input text and converts it into a context-rich vector representation. The decoder then generates the translated text by predicting each word sequentially, using the context vectors and attention to focus on relevant parts of the input sentence.