Fully-parallel text generation for neural machine translation

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. One area where deep learning has led to especially impressive results is tasks that require a machine to generate natural language text; two of these

New Neural Network Building Block Allows Faster and More Accurate Text Understanding

Block diagram illustrating the QRNN architecture (right), compared to traditional architectures for text (left) and images (center).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

MetaMind Neural Machine Translation System for WMT 2016

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. We integrate promising recent developments in NMT, including