@inproceedings{raganato-etal-2018-university,
title = "The {U}niversity of {H}elsinki submissions to the {WMT}18 news task",
author = {Raganato, Alessandro and
Scherrer, Yves and
Nieminen, Tommi and
Hurskainen, Arvi and
Tiedemann, J{\"o}rg},
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/W18-6425",
doi = "10.18653/v1/W18-6425",
pages = "488--495",
abstract = "This paper describes the University of Helsinki{'}s submissions to the WMT18 shared news translation task for English-Finnish and English-Estonian, in both directions. This year, our main submissions employ a novel neural architecture, the Transformer, using the open-source OpenNMT framework. Our experiments couple domain labeling and fine tuned multilingual models with shared vocabularies between the source and target language, using the provided parallel data of the shared task and additional back-translations. Finally, we compare, for the English-to-Finnish case, the effectiveness of different machine translation architectures, starting from a rule-based approach to our best neural model, analyzing the output and highlighting future research.",
}
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%0 Conference Proceedings
%T The University of Helsinki submissions to the WMT18 news task
%A Raganato, Alessandro
%A Scherrer, Yves
%A Nieminen, Tommi
%A Hurskainen, Arvi
%A Tiedemann, Jörg
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F raganato-etal-2018-university
%X This paper describes the University of Helsinki’s submissions to the WMT18 shared news translation task for English-Finnish and English-Estonian, in both directions. This year, our main submissions employ a novel neural architecture, the Transformer, using the open-source OpenNMT framework. Our experiments couple domain labeling and fine tuned multilingual models with shared vocabularies between the source and target language, using the provided parallel data of the shared task and additional back-translations. Finally, we compare, for the English-to-Finnish case, the effectiveness of different machine translation architectures, starting from a rule-based approach to our best neural model, analyzing the output and highlighting future research.
%R 10.18653/v1/W18-6425
%U https://aclanthology.org/W18-6425
%U https://doi.org/10.18653/v1/W18-6425
%P 488-495
Markdown (Informal)
[The University of Helsinki submissions to the WMT18 news task](https://aclanthology.org/W18-6425) (Raganato et al., 2018)
ACL
- Alessandro Raganato, Yves Scherrer, Tommi Nieminen, Arvi Hurskainen, and Jörg Tiedemann. 2018. The University of Helsinki submissions to the WMT18 news task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 488–495, Belgium, Brussels. Association for Computational Linguistics.