Abstract
This paper presents the NICT’s participation in the WMT18 shared parallel corpus filtering task. The organizers provided 1 billion words German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural machine translation (NMT) system. Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data. Finally, we sampled 100 million and 10 million words and built corresponding NMT systems. Empirical results show that our NMT systems trained on sampled data achieve promising performance.- Anthology ID:
- W18-6489
- 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:
- 963–967
- Language:
- URL:
- https://aclanthology.org/W18-6489
- DOI:
- 10.18653/v1/W18-6489
- Bibkey:
- Cite (ACL):
- Rui Wang, Benjamin Marie, Masao Utiyama, and Eiichiro Sumita. 2018. NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 963–967, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task (Wang et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6489.pdf
Export citation
@inproceedings{wang-etal-2018-nicts,
title = "{NICT}{'}s Corpus Filtering Systems for the {WMT}18 Parallel Corpus Filtering Task",
author = "Wang, Rui and
Marie, Benjamin and
Utiyama, Masao and
Sumita, Eiichiro",
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/W18-6489",
doi = "10.18653/v1/W18-6489",
pages = "963--967",
abstract = "This paper presents the NICT{'}s participation in the WMT18 shared parallel corpus filtering task. The organizers provided 1 billion words German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural machine translation (NMT) system. Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data. Finally, we sampled 100 million and 10 million words and built corresponding NMT systems. Empirical results show that our NMT systems trained on sampled data achieve promising performance.",
}
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%0 Conference Proceedings %T NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task %A Wang, Rui %A Marie, Benjamin %A Utiyama, Masao %A Sumita, Eiichiro %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 wang-etal-2018-nicts %X This paper presents the NICT’s participation in the WMT18 shared parallel corpus filtering task. The organizers provided 1 billion words German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural machine translation (NMT) system. Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data. Finally, we sampled 100 million and 10 million words and built corresponding NMT systems. Empirical results show that our NMT systems trained on sampled data achieve promising performance. %R 10.18653/v1/W18-6489 %U https://aclanthology.org/W18-6489 %U https://doi.org/10.18653/v1/W18-6489 %P 963-967
Markdown (Informal)
[NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task](https://aclanthology.org/W18-6489) (Wang et al., WMT 2018)
- NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task (Wang et al., WMT 2018)
ACL
- Rui Wang, Benjamin Marie, Masao Utiyama, and Eiichiro Sumita. 2018. NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 963–967, Belgium, Brussels. Association for Computational Linguistics.