The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Graça, Aman Gokrani, Hermann Ney
Abstract
This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2% BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8% BLEU.- Anthology ID:
- W18-6487
- 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:
- 946–954
- Language:
- URL:
- https://aclanthology.org/W18-6487
- DOI:
- 10.18653/v1/W18-6487
- Bibkey:
- Cite (ACL):
- Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Graça, Aman Gokrani, and Hermann Ney. 2018. The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 946–954, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task (Rossenbach et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6487.pdf
Export citation
@inproceedings{rossenbach-etal-2018-rwth,
title = "The {RWTH} {A}achen {U}niversity Filtering System for the {WMT} 2018 Parallel Corpus Filtering Task",
author = "Rossenbach, Nick and
Rosendahl, Jan and
Kim, Yunsu and
Gra{\c{c}}a, Miguel and
Gokrani, Aman and
Ney, Hermann",
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/W18-6487",
doi = "10.18653/v1/W18-6487",
pages = "946--954",
abstract = "This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2{\%} BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8{\%} BLEU.",
}
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%0 Conference Proceedings %T The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task %A Rossenbach, Nick %A Rosendahl, Jan %A Kim, Yunsu %A Graça, Miguel %A Gokrani, Aman %A Ney, Hermann %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 rossenbach-etal-2018-rwth %X This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2% BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8% BLEU. %R 10.18653/v1/W18-6487 %U https://aclanthology.org/W18-6487 %U https://doi.org/10.18653/v1/W18-6487 %P 946-954
Markdown (Informal)
[The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task](https://aclanthology.org/W18-6487) (Rossenbach et al., WMT 2018)
- The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task (Rossenbach et al., WMT 2018)
ACL
- Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Graça, Aman Gokrani, and Hermann Ney. 2018. The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 946–954, Belgium, Brussels. Association for Computational Linguistics.