Modular Septilingual Neural Machine Translation
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Currently, the majority of state-of-the-art multilingual neural machine translation
systems use a single universal model which fully shares parameters between all
language pairs. The University of Tartu Neural Machine Translation system uses the
universal architecture as well, and thus also suffers from the problems associated with it,
such as limited capacity per language pair. Previous research has shown that a modularized
approach with language-specific encoders and decoders successfully addresses many
of the universal model’s shortcomings. This thesis applies the modularized architecture
and improves the University of Tartu translation system. Orders of magnitude larger
dataset containing 7 languages is used to train the models compared to previous work.
The modularized model achieves significantly higher BLEU scores than the University
of Tartu model and the baseline universal model on all language pairs.
Description
Keywords
machine translation, multilingual machine translation, neural machine translation, neural networks, natural language processing