Abstract
We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.- Anthology ID:
- W18-6422
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 467–472
- Language:
- URL:
- https://aclanthology.org/W18-6422
- DOI:
- 10.18653/v1/W18-6422
- Bibkey:
- Cite (ACL):
- Ngoc-Quan Pham, Jan Niehues, and Alexander Waibel. 2018. The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 467–472, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018 (Pham et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6422.pdf
Export citation
@inproceedings{pham-etal-2018-karlsruhe,
title = "The Karlsruhe Institute of Technology Systems for the News Translation Task in {WMT} 2018",
author = "Pham, Ngoc-Quan and
Niehues, Jan and
Waibel, Alexander",
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
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
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/W18-6422",
doi = "10.18653/v1/W18-6422",
pages = "467--472",
abstract = "We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.",
}
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%0 Conference Proceedings %T The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018 %A Pham, Ngoc-Quan %A Niehues, Jan %A Waibel, Alexander %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 Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Specia, Lucia %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Third Conference on Machine Translation: Shared Task Papers %D 2018 %8 October %I Association for Computational Linguistics %C Belgium, Brussels %F pham-etal-2018-karlsruhe %X We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points. %R 10.18653/v1/W18-6422 %U https://aclanthology.org/W18-6422 %U https://doi.org/10.18653/v1/W18-6422 %P 467-472
Markdown (Informal)
[The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018](https://aclanthology.org/W18-6422) (Pham et al., WMT 2018)
- The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018 (Pham et al., WMT 2018)
ACL
- Ngoc-Quan Pham, Jan Niehues, and Alexander Waibel. 2018. The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 467–472, Belgium, Brussels. Association for Computational Linguistics.