Transformer Architecture revolutionized natural language processing by introducing self-attention mechanisms, allowing models to weigh the significance of different words in a sentence contextually. This architecture enables parallelization and scalability, leading to more efficient training and superior performance in various tasks compared to previous models like RNNs and LSTMs.