Abstract
In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively.- Anthology ID:
- 2020.wmt-1.81
- 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:
- 772–776
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.81
- DOI:
- Bibkey:
- Cite (ACL):
- Dongjun Lee. 2020. Cross-Lingual Transformers for Neural Automatic Post-Editing. In Proceedings of the Fifth Conference on Machine Translation, pages 772–776, Online. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Transformers for Neural Automatic Post-Editing (Lee, WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.81.pdf
- Video:
- https://slideslive.com/38939547
Export citation
@inproceedings{lee-2020-cross,
title = "Cross-Lingual Transformers for Neural Automatic Post-Editing",
author = "Lee, Dongjun",
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-1.81",
pages = "772--776",
abstract = "In this paper, we describe the Bering Lab{'}s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively.",
}
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%0 Conference Proceedings %T Cross-Lingual Transformers for Neural Automatic Post-Editing %A Lee, Dongjun %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 lee-2020-cross %X In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively. %U https://aclanthology.org/2020.wmt-1.81 %P 772-776
Markdown (Informal)
[Cross-Lingual Transformers for Neural Automatic Post-Editing](https://aclanthology.org/2020.wmt-1.81) (Lee, WMT 2020)
- Cross-Lingual Transformers for Neural Automatic Post-Editing (Lee, WMT 2020)
ACL
- Dongjun Lee. 2020. Cross-Lingual Transformers for Neural Automatic Post-Editing. In Proceedings of the Fifth Conference on Machine Translation, pages 772–776, Online. Association for Computational Linguistics.