Transitioning entirely to neural machine translation

This post details the significant transition from phrase-based statistical machine translation to neural machine translation (NMT) for all of Facebook's backend translation systems. It highlights the use of sequence-to-sequence LSTMs with attention for improved context handling and accuracy, techniques for managing unknown words and vocabulary reduction, and the scaling of these models using Caffe2. The post also introduces the application of convolutional neural networks (CNNs) for translation and the ongoing exploration of multilingual models, marking a major leap in translation quality and efficiency.