Low-Resource Translation as Language Modeling

Tucker Berckmann; Berkan Hiziroglu

doi:10.18653/v1/2020.wmt-1.127

Low-Resource Translation as Language Modeling

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Abstract

We present our submission to the very low resource supervised machine translation task at WMT20. We use a decoder-only transformer architecture and formulate the translation task as language modeling. To address the low-resource aspect of the problem, we pretrain over a similar language parallel corpus. Then, we employ an intermediate back-translation step before fine-tuning. Finally, we present an analysis of the system’s performance.

Anthology ID:: 2020.wmt-1.127
Volume:: Proceedings of the Fifth Conference on Machine Translation
Month:: November
Year:: 2020
Address:: Online
Editors:: Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
Venue:: WMT
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1079–1083
Language:
URL:: https://aclanthology.org/2020.wmt-1.127/
DOI:: 10.18653/v1/2020.wmt-1.127
Bibkey:
Cite (ACL):: Tucker Berckmann and Berkan Hiziroglu. 2020. Low-Resource Translation as Language Modeling. In Proceedings of the Fifth Conference on Machine Translation, pages 1079–1083, Online. Association for Computational Linguistics.
Cite (Informal):: Low-Resource Translation as Language Modeling (Berckmann & Hiziroglu, WMT 2020)
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PDF:: https://aclanthology.org/2020.wmt-1.127.pdf
Video:: https://slideslive.com/38939598

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@inproceedings{berckmann-hiziroglu-2020-low,
    title = "Low-Resource Translation as Language Modeling",
    author = "Berckmann, Tucker  and
      Hiziroglu, Berkan",
    editor = {Barrault, Lo{\"i}c  and
      Bojar, Ond{\v{r}}ej  and
      Bougares, Fethi  and
      Chatterjee, Rajen  and
      Costa-juss{\`a}, Marta R.  and
      Federmann, Christian  and
      Fishel, Mark  and
      Fraser, Alexander  and
      Graham, Yvette  and
      Guzman, Paco  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Morishita, Makoto  and
      Monz, Christof  and
      Nagata, Masaaki  and
      Nakazawa, Toshiaki  and
      Negri, Matteo},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.127/",
    doi = "10.18653/v1/2020.wmt-1.127",
    pages = "1079--1083",
    abstract = "We present our submission to the very low resource supervised machine translation task at WMT20. We use a decoder-only transformer architecture and formulate the translation task as language modeling. To address the low-resource aspect of the problem, we pretrain over a similar language parallel corpus. Then, we employ an intermediate back-translation step before fine-tuning. Finally, we present an analysis of the system{'}s performance."
}

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%0 Conference Proceedings
%T Low-Resource Translation as Language Modeling
%A Berckmann, Tucker
%A Hiziroglu, Berkan
%Y Barrault, Loïc
%Y Bojar, Ondřej
%Y Bougares, Fethi
%Y Chatterjee, Rajen
%Y Costa-jussà, Marta R.
%Y Federmann, Christian
%Y Fishel, Mark
%Y Fraser, Alexander
%Y Graham, Yvette
%Y Guzman, Paco
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Martins, André
%Y Morishita, Makoto
%Y Monz, Christof
%Y Nagata, Masaaki
%Y Nakazawa, Toshiaki
%Y Negri, Matteo
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F berckmann-hiziroglu-2020-low
%X We present our submission to the very low resource supervised machine translation task at WMT20. We use a decoder-only transformer architecture and formulate the translation task as language modeling. To address the low-resource aspect of the problem, we pretrain over a similar language parallel corpus. Then, we employ an intermediate back-translation step before fine-tuning. Finally, we present an analysis of the system’s performance.
%R 10.18653/v1/2020.wmt-1.127
%U https://aclanthology.org/2020.wmt-1.127/
%U https://doi.org/10.18653/v1/2020.wmt-1.127
%P 1079-1083

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Markdown (Informal)

[Low-Resource Translation as Language Modeling](https://aclanthology.org/2020.wmt-1.127/) (Berckmann & Hiziroglu, WMT 2020)

Low-Resource Translation as Language Modeling (Berckmann & Hiziroglu, WMT 2020)

ACL

Tucker Berckmann and Berkan Hiziroglu. 2020. Low-Resource Translation as Language Modeling. In Proceedings of the Fifth Conference on Machine Translation, pages 1079–1083, Online. Association for Computational Linguistics.