@inproceedings{przybysz-etal-2017-samsung,
title = "The {S}amsung and {U}niversity of {E}dinburgh{'}s submission to {IWSLT}17",
author = "Przybysz, Pawel and
Chochowski, Marcin and
Sennrich, Rico and
Haddow, Barry and
Birch, Alexandra",
editor = "Sakti, Sakriani and
Utiyama, Masao",
booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation",
month = dec # " 14-15",
year = "2017",
address = "Tokyo, Japan",
publisher = "International Workshop on Spoken Language Translation",
url = "https://aclanthology.org/2017.iwslt-1.3",
pages = "23--28",
abstract = "This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in two translation directions, en-de and de-en. We also participated in the en-de and de-en lectures SLT task. The models have been trained with an attentional encoder-decoder model using the BiDeep model in Nematus. We filtered the training data to reduce the problem of noisy data, and we use back-translated monolingual data for domain-adaptation. We demonstrate the effectiveness of the different techniques that we applied via ablation studies. Our submission system outperforms our baseline, and last year{'}s University of Edinburgh submission to IWSLT, by more than 5 BLEU.",
}
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<abstract>This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in two translation directions, en-de and de-en. We also participated in the en-de and de-en lectures SLT task. The models have been trained with an attentional encoder-decoder model using the BiDeep model in Nematus. We filtered the training data to reduce the problem of noisy data, and we use back-translated monolingual data for domain-adaptation. We demonstrate the effectiveness of the different techniques that we applied via ablation studies. Our submission system outperforms our baseline, and last year’s University of Edinburgh submission to IWSLT, by more than 5 BLEU.</abstract>
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%0 Conference Proceedings
%T The Samsung and University of Edinburgh’s submission to IWSLT17
%A Przybysz, Pawel
%A Chochowski, Marcin
%A Sennrich, Rico
%A Haddow, Barry
%A Birch, Alexandra
%Y Sakti, Sakriani
%Y Utiyama, Masao
%S Proceedings of the 14th International Conference on Spoken Language Translation
%D 2017
%8 dec 14 15
%I International Workshop on Spoken Language Translation
%C Tokyo, Japan
%F przybysz-etal-2017-samsung
%X This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in two translation directions, en-de and de-en. We also participated in the en-de and de-en lectures SLT task. The models have been trained with an attentional encoder-decoder model using the BiDeep model in Nematus. We filtered the training data to reduce the problem of noisy data, and we use back-translated monolingual data for domain-adaptation. We demonstrate the effectiveness of the different techniques that we applied via ablation studies. Our submission system outperforms our baseline, and last year’s University of Edinburgh submission to IWSLT, by more than 5 BLEU.
%U https://aclanthology.org/2017.iwslt-1.3
%P 23-28
Markdown (Informal)
[The Samsung and University of Edinburgh’s submission to IWSLT17](https://aclanthology.org/2017.iwslt-1.3) (Przybysz et al., IWSLT 2017)
ACL
- Pawel Przybysz, Marcin Chochowski, Rico Sennrich, Barry Haddow, and Alexandra Birch. 2017. The Samsung and University of Edinburgh’s submission to IWSLT17. In Proceedings of the 14th International Conference on Spoken Language Translation, pages 23–28, Tokyo, Japan. International Workshop on Spoken Language Translation.