Over the past few years, neural networks have driven rapid improvements in accuracy and quality for natural language tasks like text classification and question answering.
In deep learning, there are two very different ways to process input (like an image or a document). Typically, images are processed all at once, with the same kind of computation happening for every part of the image simultaneously.
Neural Machine Translation (NMT) systems, introduced only in 2013, have achieved state of the art results in many MT tasks. MetaMind’s submissions to WMT ’16 seek to push the state of the art in one such task, English→German newsdomain translation.
