@inproceedings{scherrer-etal-2019-university,
title = "The {U}niversity of {H}elsinki Submissions to the {WMT}19 Similar Language Translation Task",
author = "Scherrer, Yves and
V{\'a}zquez, Ra{\'u}l and
Virpioja, Sami",
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
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5432",
doi = "10.18653/v1/W19-5432",
pages = "236--244",
abstract = "This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech {\textless}-{\textgreater} Polish and Spanish {\textless}-{\textgreater} Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding.",
}
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<abstract>This paper describes the University of Helsinki Language Technology group’s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech \textless-\textgreater Polish and Spanish \textless-\textgreater Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding.</abstract>
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%0 Conference Proceedings
%T The University of Helsinki Submissions to the WMT19 Similar Language Translation Task
%A Scherrer, Yves
%A Vázquez, Raúl
%A Virpioja, Sami
%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 Martins, André
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F scherrer-etal-2019-university
%X This paper describes the University of Helsinki Language Technology group’s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech \textless-\textgreater Polish and Spanish \textless-\textgreater Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding.
%R 10.18653/v1/W19-5432
%U https://aclanthology.org/W19-5432
%U https://doi.org/10.18653/v1/W19-5432
%P 236-244
Markdown (Informal)
[The University of Helsinki Submissions to the WMT19 Similar Language Translation Task](https://aclanthology.org/W19-5432) (Scherrer et al., WMT 2019)
ACL