Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to the state-of-the-art in the WMT task.
@article{arxiv.1907.00735,
title = {From Bilingual to Multilingual Neural Machine Translation by Incremental Training},
author = {Carlos Escolano and Marta R. Costa-Jussà and José A. R. Fonollosa},
journal= {arXiv preprint arXiv:1907.00735},
year = {2019}
}
Comments
Accepted paper at ACL 2019 Student Research Workshop. arXiv admin note: substantial text overlap with arXiv:1905.06831