CUED@WMT19:EWC&LMs

Felix Stahlberg; Danielle Saunders; Adrià de Gispert; Bill Byrne

doi:10.18653/v1/W19-5340

CUED@WMT19:EWC&LMs

Felix Stahlberg, Danielle Saunders, Adrià de Gispert, Bill Byrne

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Abstract

Two techniques provide the fabric of the Cambridge University Engineering Department’s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs). We report substantial gains by fine-tuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned n-gram LM.

Anthology ID:: W19-5340
Volume:: Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:: August
Year:: 2019
Address:: Florence, Italy
Editors:: Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
Venue:: WMT
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 364–373
Language:
URL:: https://aclanthology.org/W19-5340/
DOI:: 10.18653/v1/W19-5340
Bibkey:
Cite (ACL):: Felix Stahlberg, Danielle Saunders, Adrià de Gispert, and Bill Byrne. 2019. CUED@WMT19:EWC&LMs. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 364–373, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):: CUED@WMT19:EWC&LMs (Stahlberg et al., WMT 2019)
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PDF:: https://aclanthology.org/W19-5340.pdf

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@inproceedings{stahlberg-etal-2019-cued,
    title = "{CUED}@{WMT}19:{EWC}{\&}{LM}s",
    author = "Stahlberg, Felix  and
      Saunders, Danielle  and
      de Gispert, Adri{\`a}  and
      Byrne, Bill",
    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
      Martins, Andr{\'e}  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-5340/",
    doi = "10.18653/v1/W19-5340",
    pages = "364--373",
    abstract = "Two techniques provide the fabric of the Cambridge University Engineering Department{'}s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs). We report substantial gains by fine-tuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned n-gram LM."
}

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%0 Conference Proceedings
%T CUED@WMT19:EWC&LMs
%A Stahlberg, Felix
%A Saunders, Danielle
%A de Gispert, Adrià
%A Byrne, Bill
%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 Martins, André
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F stahlberg-etal-2019-cued
%X Two techniques provide the fabric of the Cambridge University Engineering Department’s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs). We report substantial gains by fine-tuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned n-gram LM.
%R 10.18653/v1/W19-5340
%U https://aclanthology.org/W19-5340/
%U https://doi.org/10.18653/v1/W19-5340
%P 364-373

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Markdown (Informal)

[CUED@WMT19:EWC&LMs](https://aclanthology.org/W19-5340/) (Stahlberg et al., WMT 2019)

CUED@WMT19:EWC&LMs (Stahlberg et al., WMT 2019)

ACL

Felix Stahlberg, Danielle Saunders, Adrià de Gispert, and Bill Byrne. 2019. CUED@WMT19:EWC&LMs. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 364–373, Florence, Italy. Association for Computational Linguistics.