@inproceedings{grozea-2019-system,
title = "System Description: The Submission of {FOKUS} to the {WMT} 19 Robustness Task",
author = "Grozea, Cristian",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5363",
doi = "10.18653/v1/W19-5363",
pages = "537--538",
abstract = "This paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task. We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs. The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task. These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data. The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.",
}
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<abstract>This paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task. We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs. The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task. These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data. The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.</abstract>
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%0 Conference Proceedings
%T System Description: The Submission of FOKUS to the WMT 19 Robustness Task
%A Grozea, Cristian
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F grozea-2019-system
%X This paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task. We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs. The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task. These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data. The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.
%R 10.18653/v1/W19-5363
%U https://aclanthology.org/W19-5363
%U https://doi.org/10.18653/v1/W19-5363
%P 537-538
Markdown (Informal)
[System Description: The Submission of FOKUS to the WMT 19 Robustness Task](https://aclanthology.org/W19-5363) (Grozea, 2019)
ACL