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)
- Copy Citation:
- PDF:
- https://aclanthology.org/W19-5340.pdf
Export citation
@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
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.