Tilde’s Parallel Corpus Filtering Methods for WMT 2018

Mārcis Pinnis

doi:10.18653/v1/W18-6486

Tilde’s Parallel Corpus Filtering Methods for WMT 2018

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Abstract

The paper describes parallel corpus filtering methods that allow reducing noise of noisy “parallel” corpora from a level where the corpora are not usable for neural machine translation training (i.e., the resulting systems fail to achieve reasonable translation quality; well below 10 BLEU points) up to a level where the trained systems show decent (over 20 BLEU points on a 10 million word dataset and up to 30 BLEU points on a 100 million word dataset). The paper also documents Tilde’s submissions to the WMT 2018 shared task on parallel corpus filtering.

Anthology ID:: W18-6486
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:: 939–945
Language:
URL:: https://aclanthology.org/W18-6486/
DOI:: 10.18653/v1/W18-6486
Bibkey:
Cite (ACL):: Mārcis Pinnis. 2018. Tilde’s Parallel Corpus Filtering Methods for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 939–945, Belgium, Brussels. Association for Computational Linguistics.
Cite (Informal):: Tilde’s Parallel Corpus Filtering Methods for WMT 2018 (Pinnis, WMT 2018)
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PDF:: https://aclanthology.org/W18-6486.pdf

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@inproceedings{pinnis-2018-tildes,
    title = "Tilde{'}s Parallel Corpus Filtering Methods for {WMT} 2018",
    author = "Pinnis, M{\={a}}rcis",
    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/W18-6486/",
    doi = "10.18653/v1/W18-6486",
    pages = "939--945",
    abstract = "The paper describes parallel corpus filtering methods that allow reducing noise of noisy ``parallel'' corpora from a level where the corpora are not usable for neural machine translation training (i.e., the resulting systems fail to achieve reasonable translation quality; well below 10 BLEU points) up to a level where the trained systems show decent (over 20 BLEU points on a 10 million word dataset and up to 30 BLEU points on a 100 million word dataset). The paper also documents Tilde{'}s submissions to the WMT 2018 shared task on parallel corpus filtering."
}

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%0 Conference Proceedings
%T Tilde’s Parallel Corpus Filtering Methods for WMT 2018
%A Pinnis, Mārcis
%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 pinnis-2018-tildes
%X The paper describes parallel corpus filtering methods that allow reducing noise of noisy “parallel” corpora from a level where the corpora are not usable for neural machine translation training (i.e., the resulting systems fail to achieve reasonable translation quality; well below 10 BLEU points) up to a level where the trained systems show decent (over 20 BLEU points on a 10 million word dataset and up to 30 BLEU points on a 100 million word dataset). The paper also documents Tilde’s submissions to the WMT 2018 shared task on parallel corpus filtering.
%R 10.18653/v1/W18-6486
%U https://aclanthology.org/W18-6486/
%U https://doi.org/10.18653/v1/W18-6486
%P 939-945

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Markdown (Informal)

[Tilde’s Parallel Corpus Filtering Methods for WMT 2018](https://aclanthology.org/W18-6486/) (Pinnis, WMT 2018)

Tilde’s Parallel Corpus Filtering Methods for WMT 2018 (Pinnis, WMT 2018)

ACL

Mārcis Pinnis. 2018. Tilde’s Parallel Corpus Filtering Methods for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 939–945, Belgium, Brussels. Association for Computational Linguistics.