<ul><li>Neural Machine Translation employs deep learning techniques, utilizing extensive datasets of translated sentences to train models capable of translating between various languages.</li><li>The Encoder-Decoder structure is a traditional and well-established version of NMT, consisting of two recurrent neural networks (RNN) that work together to form a translation model.</li><li>The encoder processes the input sequence to generate a set of context vectors, which are then used by the decoder to produce an output sequence.</li><li>The incorporation of attention mechanisms in the encoder-decoder architecture enables the model to focus on specific parts of the input for better translation accuracy, especially in longer sentences.</li></ul>

Neural Machine Translation: Unveiling the Encoder-Decoder Architecture

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