Practical Neural Machine Translation
Abstract
AbstractNeural Machine Translation (NMT) has achieved new breakthroughs in machine translation in recent years. It has dominated recent shared translation tasks in machine translation research, and is also being quickly adopted in industry. The technical differences between NMT and the previously dominant phrase-based statistical approach require that practictioners learn new best practices for building MT systems, ranging from different hardware requirements, new techniques for handling rare words and monolingual data, to new opportunities in continued learning and domain adaptation.This tutorial is aimed at researchers and users of machine translation interested in working with NMT. The tutorial will cover a basic theoretical introduction to NMT, discuss the components of state-of-the-art systems, and provide practical advice for building NMT systems.