@inproceedings{scherrer-2018-university,
title = "The {U}niversity of {H}elsinki submissions to the {IWSLT} 2018 low-resource translation task",
author = "Scherrer, Yves",
booktitle = "Proceedings of the 15th International Conference on Spoken Language Translation",
month = oct # " 29-30",
year = "2018",
address = "Brussels",
publisher = "International Conference on Spoken Language Translation",
url = "https://aclanthology.org/2018.iwslt-1.12",
pages = "82--88",
abstract = "This paper presents the University of Helsinki submissions to the Basque{--}English low-resource translation task. Our primary system is a standard bilingual Transformer system, trained on the available parallel data and various types of synthetic data. We describe the creation of the synthetic datasets, some of which use a pivoting approach, in detail. One of our contrastive submissions is a multilingual model trained on comparable data, but without the synthesized parts. Our bilingual model with synthetic data performed best, obtaining 25.25 BLEU on the test data.",
}
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<abstract>This paper presents the University of Helsinki submissions to the Basque–English low-resource translation task. Our primary system is a standard bilingual Transformer system, trained on the available parallel data and various types of synthetic data. We describe the creation of the synthetic datasets, some of which use a pivoting approach, in detail. One of our contrastive submissions is a multilingual model trained on comparable data, but without the synthesized parts. Our bilingual model with synthetic data performed best, obtaining 25.25 BLEU on the test data.</abstract>
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%0 Conference Proceedings
%T The University of Helsinki submissions to the IWSLT 2018 low-resource translation task
%A Scherrer, Yves
%S Proceedings of the 15th International Conference on Spoken Language Translation
%D 2018
%8 oct" 29 30"
%I International Conference on Spoken Language Translation
%C Brussels
%F scherrer-2018-university
%X This paper presents the University of Helsinki submissions to the Basque–English low-resource translation task. Our primary system is a standard bilingual Transformer system, trained on the available parallel data and various types of synthetic data. We describe the creation of the synthetic datasets, some of which use a pivoting approach, in detail. One of our contrastive submissions is a multilingual model trained on comparable data, but without the synthesized parts. Our bilingual model with synthetic data performed best, obtaining 25.25 BLEU on the test data.
%U https://aclanthology.org/2018.iwslt-1.12
%P 82-88
Markdown (Informal)
[The University of Helsinki submissions to the IWSLT 2018 low-resource translation task](https://aclanthology.org/2018.iwslt-1.12) (Scherrer, IWSLT 2018)
ACL