Abstract
Our entry to the parallel corpus filtering task uses a two-step strategy. The first step uses a series of pragmatic hard ‘rules’ to remove the worst example sentences. This first step reduces the effective corpus size down from the initial 1 billion to 160 million tokens. The second step uses four different heuristics weighted to produce a score that is then used for further filtering down to 100 or 10 million tokens. Our final system produces competitive results without requiring excessive fine tuning to the exact task or language pair. The first step in isolation provides a very fast filter that gives most of the gains of the final system.- Anthology ID:
- W18-6472
- 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:
- 853–859
- Language:
- URL:
- https://aclanthology.org/W18-6472
- DOI:
- 10.18653/v1/W18-6472
- Bibkey:
- Cite (ACL):
- Tom Ash, Remi Francis, and Will Williams. 2018. The Speechmatics Parallel Corpus Filtering System for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 853–859, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- The Speechmatics Parallel Corpus Filtering System for WMT18 (Ash et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6472.pdf
Export citation
@inproceedings{ash-etal-2018-speechmatics,
title = "The Speechmatics Parallel Corpus Filtering System for {WMT}18",
author = "Ash, Tom and
Francis, Remi and
Williams, Will",
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/W18-6472",
doi = "10.18653/v1/W18-6472",
pages = "853--859",
abstract = "Our entry to the parallel corpus filtering task uses a two-step strategy. The first step uses a series of pragmatic hard {`}rules{'} to remove the worst example sentences. This first step reduces the effective corpus size down from the initial 1 billion to 160 million tokens. The second step uses four different heuristics weighted to produce a score that is then used for further filtering down to 100 or 10 million tokens. Our final system produces competitive results without requiring excessive fine tuning to the exact task or language pair. The first step in isolation provides a very fast filter that gives most of the gains of the final system.",
}
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%0 Conference Proceedings %T The Speechmatics Parallel Corpus Filtering System for WMT18 %A Ash, Tom %A Francis, Remi %A Williams, Will %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 ash-etal-2018-speechmatics %X Our entry to the parallel corpus filtering task uses a two-step strategy. The first step uses a series of pragmatic hard ‘rules’ to remove the worst example sentences. This first step reduces the effective corpus size down from the initial 1 billion to 160 million tokens. The second step uses four different heuristics weighted to produce a score that is then used for further filtering down to 100 or 10 million tokens. Our final system produces competitive results without requiring excessive fine tuning to the exact task or language pair. The first step in isolation provides a very fast filter that gives most of the gains of the final system. %R 10.18653/v1/W18-6472 %U https://aclanthology.org/W18-6472 %U https://doi.org/10.18653/v1/W18-6472 %P 853-859
Markdown (Informal)
[The Speechmatics Parallel Corpus Filtering System for WMT18](https://aclanthology.org/W18-6472) (Ash et al., WMT 2018)
- The Speechmatics Parallel Corpus Filtering System for WMT18 (Ash et al., WMT 2018)
ACL
- Tom Ash, Remi Francis, and Will Williams. 2018. The Speechmatics Parallel Corpus Filtering System for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 853–859, Belgium, Brussels. Association for Computational Linguistics.