@inproceedings{garcia-etal-2021-towards,
title = "Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution",
author = "Garcia, Xavier and
Constant, Noah and
Parikh, Ankur and
Firat, Orhan",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.93/",
doi = "10.18653/v1/2021.naacl-main.93",
pages = "1184--1192",
abstract = "We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages."
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%0 Conference Proceedings
%T Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution
%A Garcia, Xavier
%A Constant, Noah
%A Parikh, Ankur
%A Firat, Orhan
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F garcia-etal-2021-towards
%X We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages.
%R 10.18653/v1/2021.naacl-main.93
%U https://aclanthology.org/2021.naacl-main.93/
%U https://doi.org/10.18653/v1/2021.naacl-main.93
%P 1184-1192
Markdown (Informal)
[Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution](https://aclanthology.org/2021.naacl-main.93/) (Garcia et al., NAACL 2021)
ACL