Neural Machine Translation Enhancements through Lexical Semantic Network

Abstract

In most languages, many words have multiple senses, thus machine translation systems have to choose between several candidates representing different senses of an input word. Although neural machine translation has recently become a dominant paradigm and achieved great progress, it still has to confront with the challenge of word sense disambiguation. Neural machine translation models are trained to identify the correct sense of a word as part of an end-to-end translation task, and their performances on word sense disambiguation are not satisfactory. This paper presents a case study of machine translation for Korean language. We have manually built a Korean lexical semantic network - UWordMap - as a large-scale lexical semantic knowledge-based in which each sense of every polysemous word is associated with a sense-code constituting a network node. Then, based on UWordMap, we determine the correct sense and tag the appropriated sense-code for polysemous words of the training corpus before training neural machine translation models. Experiments on translation from Korean to English and Vietnamese show that UWordMap can significantly improve quality of Korean neural machine translation systems in terms of BLEU and TER cores.