A Framework for Hierarchical Multilingual Machine Translation
Abstract
Multilingual machine translation has recently been in vogue given its potential for improving machine translation performance for low-resource languages via transfer learning. Empirical examinations demonstrating the success of existing multilingual machine translation strategies, however, are limited to experiments in specific language groups. In this paper, we present a hierarchical framework for building multilingual machine translation strategies that takes advantage of a typological language family tree for enabling transfer among similar languages while avoiding the negative effects that result from incorporating languages that are too different to each other. Exhaustive experimentation on a dataset with 41 languages demonstrates the validity of the proposed framework, especially when it comes to improving the performance of low-resource languages via the use of typologically related families for which richer sets of resources are available.
Cite
@article{arxiv.2005.05507,
title = {A Framework for Hierarchical Multilingual Machine Translation},
author = {Ion Madrazo Azpiazu and Maria Soledad Pera},
journal= {arXiv preprint arXiv:2005.05507},
year = {2020}
}