@inproceedings{jon-etal-2023-cuni,
title = "{CUNI} at {WMT}23 General Translation Task: {MT} and a Genetic Algorithm",
author = "Jon, Josef and
Popel, Martin and
Bojar, Ond{\v{r}}ej",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.8",
doi = "10.18653/v1/2023.wmt-1.8",
pages = "119--127",
abstract = "This paper presents the contributions of Charles University teams to the WMT23 General translation task (English to Czech and Czech to Ukrainian translation directions). Our main submission, CUNI-GA, is a result of applying a novel n-best list reranking and modification method on translation candidates produced by the two other submitted systems, CUNI-Transformer and CUNI-DocTransformer (document-level translation only used for the $en \rightarrow cs$ direction). Our method uses a genetic algorithm and MBR decoding to search for optimal translation under a given metric (in our case, a weighted combination of ChrF, BLEU, COMET22-DA, and COMET22-QE-DA). Our submissions are first in the constrained track and show competitive performance against top-tier unconstrained systems across various automatic metrics.",
}
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<abstract>This paper presents the contributions of Charles University teams to the WMT23 General translation task (English to Czech and Czech to Ukrainian translation directions). Our main submission, CUNI-GA, is a result of applying a novel n-best list reranking and modification method on translation candidates produced by the two other submitted systems, CUNI-Transformer and CUNI-DocTransformer (document-level translation only used for the en \rightarrow cs direction). Our method uses a genetic algorithm and MBR decoding to search for optimal translation under a given metric (in our case, a weighted combination of ChrF, BLEU, COMET22-DA, and COMET22-QE-DA). Our submissions are first in the constrained track and show competitive performance against top-tier unconstrained systems across various automatic metrics.</abstract>
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%0 Conference Proceedings
%T CUNI at WMT23 General Translation Task: MT and a Genetic Algorithm
%A Jon, Josef
%A Popel, Martin
%A Bojar, Ondřej
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jon-etal-2023-cuni
%X This paper presents the contributions of Charles University teams to the WMT23 General translation task (English to Czech and Czech to Ukrainian translation directions). Our main submission, CUNI-GA, is a result of applying a novel n-best list reranking and modification method on translation candidates produced by the two other submitted systems, CUNI-Transformer and CUNI-DocTransformer (document-level translation only used for the en \rightarrow cs direction). Our method uses a genetic algorithm and MBR decoding to search for optimal translation under a given metric (in our case, a weighted combination of ChrF, BLEU, COMET22-DA, and COMET22-QE-DA). Our submissions are first in the constrained track and show competitive performance against top-tier unconstrained systems across various automatic metrics.
%R 10.18653/v1/2023.wmt-1.8
%U https://aclanthology.org/2023.wmt-1.8
%U https://doi.org/10.18653/v1/2023.wmt-1.8
%P 119-127
Markdown (Informal)
[CUNI at WMT23 General Translation Task: MT and a Genetic Algorithm](https://aclanthology.org/2023.wmt-1.8) (Jon et al., WMT 2023)
ACL