Abstract
We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.- Anthology ID:
- W19-5364
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 539–543
- Language:
- URL:
- https://aclanthology.org/W19-5364
- DOI:
- 10.18653/v1/W19-5364
- Bibkey:
- Cite (ACL):
- Jindřich Helcl, Jindřich Libovický, and Martin Popel. 2019. CUNI System for the WMT19 Robustness Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 539–543, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- CUNI System for the WMT19 Robustness Task (Helcl et al., WMT 2019)
- Copy Citation:
- PDF:
- https://aclanthology.org/W19-5364.pdf
- Data
- MTNT, WMT 2014
Export citation
@inproceedings{helcl-etal-2019-cuni,
title = "{CUNI} System for the {WMT}19 Robustness Task",
author = "Helcl, Jind{\v{r}}ich and
Libovick{\'y}, Jind{\v{r}}ich and
Popel, Martin",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
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/W19-5364",
doi = "10.18653/v1/W19-5364",
pages = "539--543",
abstract = "We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.",
}
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%0 Conference Proceedings %T CUNI System for the WMT19 Robustness Task %A Helcl, Jindřich %A Libovický, Jindřich %A Popel, Martin %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1) %D 2019 %8 August %I Association for Computational Linguistics %C Florence, Italy %F helcl-etal-2019-cuni %X We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain. %R 10.18653/v1/W19-5364 %U https://aclanthology.org/W19-5364 %U https://doi.org/10.18653/v1/W19-5364 %P 539-543
Markdown (Informal)
[CUNI System for the WMT19 Robustness Task](https://aclanthology.org/W19-5364) (Helcl et al., WMT 2019)
- CUNI System for the WMT19 Robustness Task (Helcl et al., WMT 2019)
ACL
- Jindřich Helcl, Jindřich Libovický, and Martin Popel. 2019. CUNI System for the WMT19 Robustness Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 539–543, Florence, Italy. Association for Computational Linguistics.