Open AccessScaling up a Hybrid MT System: From low to full resources
Author(s) -
Vincent Vandeghinste
Publication year - 2021
Publication title -
linguistica antverpiensia new series - themes in translation studies
Language(s) - English
Resource type - Journals
ISSN - 2295-5739
DOI - 10.52034/lanstts.v8i.245
Subject(s) - machine translation , computer science , scaling , translation (biology) , natural language processing , artificial intelligence , hybrid system , quality (philosophy) , machine learning , chemistry , mathematics , biochemistry , philosophy , geometry , epistemology , messenger rna , gene
This article describes a hybrid approach to machine translation (MT) that is inspired by the rule-based, statistical, example-based, and other hybrid machine translation approaches currently used or described in academic literature. It describes how the approach was implemented for language pairs using only limited monolingual resources and hardly any parallel resources (the METIS-II system), and how it is currently implemented with rich resources on both the source and target side as well as rich parallel data (the PaCo-MT system). We aim to illustrate that a similar paradigm can be used, irrespectively of the resources available, but of course with an impact on translation quality.