@inproceedings{paetzold-2018-utfpr-wmt,
title = "{UTFPR} at {WMT} 2018: Minimalistic Supervised Corpora Filtering for Machine Translation",
author = "Paetzold, Gustavo",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6483",
doi = "10.18653/v1/W18-6483",
pages = "923--927",
abstract = "We present the UTFPR systems at the WMT 2018 parallel corpus filtering task. Our supervised approach discerns between good and bad translations by training classic binary classification models over an artificially produced binary classification dataset derived from a high-quality translation set, and a minimalistic set of 6 semantic distance features that rely only on easy-to-gather resources. We rank translations by their probability for the {``}good{''} label. Our results show that logistic regression pairs best with our approach, yielding more consistent results throughout the different settings evaluated.",
}
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%0 Conference Proceedings
%T UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation
%A Paetzold, Gustavo
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Specia, Lucia
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 October
%I Association for Computational Linguistics
%C Belgium, Brussels
%F paetzold-2018-utfpr-wmt
%X We present the UTFPR systems at the WMT 2018 parallel corpus filtering task. Our supervised approach discerns between good and bad translations by training classic binary classification models over an artificially produced binary classification dataset derived from a high-quality translation set, and a minimalistic set of 6 semantic distance features that rely only on easy-to-gather resources. We rank translations by their probability for the “good” label. Our results show that logistic regression pairs best with our approach, yielding more consistent results throughout the different settings evaluated.
%R 10.18653/v1/W18-6483
%U https://aclanthology.org/W18-6483
%U https://doi.org/10.18653/v1/W18-6483
%P 923-927
Markdown (Informal)
[UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation](https://aclanthology.org/W18-6483) (Paetzold, WMT 2018)
ACL