Making sense of neural machine translation

Abstract

The last few years have witnessed a surge in the interest of a new machine translation paradigm: neural machine translation (NMT). Neural machine translation is starting to displace its corpus-based predecessor, statistical machine translation (SMT). In this paper, I introduce NMT, and explain in detail, without the mathematical complexity, how neural machine translation systems work, how they are trained, and their main differences with SMT systems. The paper will try to decipher NMT jargon such as “distributed representations”, “deep learning”, “word embeddings”, “vectors”, “layers”, “weights”, “encoder”, “decoder”, and “attention”, and build upon these concepts, so that individual translators and professionals working for the translation industry as well as students and academics in translation studies can make sense of this new technology and know what to expect from it. Aspects such as how NMT output differs from SMT, and the hardware and software requirements of NMT, both at training time and at run time, on the translation industry, will be discussed.