Abstract
This paper describes the Transn’s submissions to the WMT2022 shared task on TranslationSuggestion. Our team participated on two tasks: Naive Translation Suggestion and TranslationSuggestion with Hints, focusing on two language directions Zh→En and En→Zh. Apart from the golden training data provided by the shared task, we utilized synthetic corpus to fine-tune on DeltaLM (∆LM), which is a pre-trained encoder-decoder language model. We applied two-stage training strategy on ∆LM and several effective methods to generate synthetic corpus, which contribute a lot to the results. According to the official evaluation results in terms of BLEU scores, our submissions in Naive Translation Suggestion En→Zh and Translation Suggestion with Hints (both Zh→En and En→Zh) ranked 1st, and Naive Translation Suggestion Zh→En also achieved comparable result to the best score.- Anthology ID:
- 2022.wmt-1.124
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1205–1210
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.124
- DOI:
- Bibkey:
- Cite (ACL):
- Mao Hongbao, Zhang Wenbo, Cai Jie, and Cheng Jianwei. 2022. Transn’s Submissions to the WMT22 Translation Suggestion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1205–1210, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Transn’s Submissions to the WMT22 Translation Suggestion Task (Hongbao et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.124.pdf
Export citation
@inproceedings{hongbao-etal-2022-transns,
title = "Transn{'}s Submissions to the {WMT}22 Translation Suggestion Task",
author = "Hongbao, Mao and
Wenbo, Zhang and
Jie, Cai and
Jianwei, Cheng",
editor = {Koehn, Philipp and
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
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.124",
pages = "1205--1210",
abstract = "This paper describes the Transn{'}s submissions to the WMT2022 shared task on TranslationSuggestion. Our team participated on two tasks: Naive Translation Suggestion and TranslationSuggestion with Hints, focusing on two language directions Zh→En and En→Zh. Apart from the golden training data provided by the shared task, we utilized synthetic corpus to fine-tune on DeltaLM (∆LM), which is a pre-trained encoder-decoder language model. We applied two-stage training strategy on ∆LM and several effective methods to generate synthetic corpus, which contribute a lot to the results. According to the official evaluation results in terms of BLEU scores, our submissions in Naive Translation Suggestion En→Zh and Translation Suggestion with Hints (both Zh→En and En→Zh) ranked 1st, and Naive Translation Suggestion Zh→En also achieved comparable result to the best score.",
}
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<abstract>This paper describes the Transn’s submissions to the WMT2022 shared task on TranslationSuggestion. Our team participated on two tasks: Naive Translation Suggestion and TranslationSuggestion with Hints, focusing on two language directions Zh→En and En→Zh. Apart from the golden training data provided by the shared task, we utilized synthetic corpus to fine-tune on DeltaLM (∆LM), which is a pre-trained encoder-decoder language model. We applied two-stage training strategy on ∆LM and several effective methods to generate synthetic corpus, which contribute a lot to the results. According to the official evaluation results in terms of BLEU scores, our submissions in Naive Translation Suggestion En→Zh and Translation Suggestion with Hints (both Zh→En and En→Zh) ranked 1st, and Naive Translation Suggestion Zh→En also achieved comparable result to the best score.</abstract>
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%0 Conference Proceedings %T Transn’s Submissions to the WMT22 Translation Suggestion Task %A Hongbao, Mao %A Wenbo, Zhang %A Jie, Cai %A Jianwei, Cheng %Y Koehn, Philipp %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 Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F hongbao-etal-2022-transns %X This paper describes the Transn’s submissions to the WMT2022 shared task on TranslationSuggestion. Our team participated on two tasks: Naive Translation Suggestion and TranslationSuggestion with Hints, focusing on two language directions Zh→En and En→Zh. Apart from the golden training data provided by the shared task, we utilized synthetic corpus to fine-tune on DeltaLM (∆LM), which is a pre-trained encoder-decoder language model. We applied two-stage training strategy on ∆LM and several effective methods to generate synthetic corpus, which contribute a lot to the results. According to the official evaluation results in terms of BLEU scores, our submissions in Naive Translation Suggestion En→Zh and Translation Suggestion with Hints (both Zh→En and En→Zh) ranked 1st, and Naive Translation Suggestion Zh→En also achieved comparable result to the best score. %U https://aclanthology.org/2022.wmt-1.124 %P 1205-1210
Markdown (Informal)
[Transn’s Submissions to the WMT22 Translation Suggestion Task](https://aclanthology.org/2022.wmt-1.124) (Hongbao et al., WMT 2022)
- Transn’s Submissions to the WMT22 Translation Suggestion Task (Hongbao et al., WMT 2022)
ACL
- Mao Hongbao, Zhang Wenbo, Cai Jie, and Cheng Jianwei. 2022. Transn’s Submissions to the WMT22 Translation Suggestion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1205–1210, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.