Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort
Vânia Mendonça, Ricardo Rei, Luisa Coheur, Alberto Sardinha, Ana Lúcia Santos
Abstract
In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing systems. In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems. To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. Our experiments on WMT’19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.
BibTeX
@inproceedings{mendonca-etal-2021-online,
title = "{O}nline {L}earning Meets {M}achine {T}ranslation Evaluation: Finding the Best Systems with the Least Human Effort",
author = "Mendon{\c{c}}a, V{\^a}nia and
Rei, Ricardo and
Coheur, Luisa and
Sardinha, Alberto and
Santos, Ana L{\'u}cia",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.242/",
doi = "10.18653/v1/2021.acl-long.242",
pages = "3105--3117"
}