NMTn

NMTn refers to Neural Machine Translation with Transformers. This is a specific architecture used in modern machine translation systems. Unlike earlier recurrent neural network (RNN) based methods, the Transformer model relies entirely on attention mechanisms to draw global dependencies between input and output. This allows for more parallelization during training and often leads to improved translation quality, especially for long sentences.

The core components of the Transformer are its encoder and decoder stacks. The encoder processes the input sequence, creating representations that capture contextual information. The decoder then uses these representations, along with its own previously generated output, to produce the translated sequence. Self-attention within both the encoder and decoder, and cross-attention between them, are crucial for this process. This architecture has become the de facto standard in many natural language processing tasks beyond translation.