@inproceedings{kocmi-etal-2018-cuni,
title = "{CUNI} Submissions in {WMT}18",
author = "Kocmi, Tom and
Sudarikov, Roman and
Bojar, Ond{\v{r}}ej",
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/W18-6416",
doi = "10.18653/v1/W18-6416",
pages = "431--437",
abstract = "We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a {``}parent model{''} for the high-resource language pair followed by adaptation on the related low-resource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.",
}
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%0 Conference Proceedings
%T CUNI Submissions in WMT18
%A Kocmi, Tom
%A Sudarikov, Roman
%A Bojar, Ondřej
%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 kocmi-etal-2018-cuni
%X We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a “parent model” for the high-resource language pair followed by adaptation on the related low-resource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.
%R 10.18653/v1/W18-6416
%U https://aclanthology.org/W18-6416
%U https://doi.org/10.18653/v1/W18-6416
%P 431-437
Markdown (Informal)
[CUNI Submissions in WMT18](https://aclanthology.org/W18-6416) (Kocmi et al., 2018)
ACL
- Tom Kocmi, Roman Sudarikov, and Ondřej Bojar. 2018. CUNI Submissions in WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 431–437, Belgium, Brussels. Association for Computational Linguistics.