BJTU-WeChat’s Systems for the WMT22 Chat Translation Task
Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou
Abstract
This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT’22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we apply the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 81.0 and 94.6 COMET scores on English-German and German-English, respectively. The COMET scores of English-German and German-English are the highest among all submissions.- Anthology ID:
- 2022.wmt-1.91
- 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:
- 955–961
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.91
- DOI:
- Bibkey:
- Cite (ACL):
- Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, and Jie Zhou. 2022. BJTU-WeChat’s Systems for the WMT22 Chat Translation Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 955–961, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- BJTU-WeChat’s Systems for the WMT22 Chat Translation Task (Liang et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.91.pdf
Export citation
@inproceedings{liang-etal-2022-bjtu,
title = "{BJTU}-{W}e{C}hat{'}s Systems for the {WMT}22 Chat Translation Task",
author = "Liang, Yunlong and
Meng, Fandong and
Xu, Jinan and
Chen, Yufeng and
Zhou, Jie",
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-1.91",
pages = "955--961",
abstract = "This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT{'}22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we apply the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 81.0 and 94.6 COMET scores on English-German and German-English, respectively. The COMET scores of English-German and German-English are the highest among all submissions.",
}
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%0 Conference Proceedings %T BJTU-WeChat’s Systems for the WMT22 Chat Translation Task %A Liang, Yunlong %A Meng, Fandong %A Xu, Jinan %A Chen, Yufeng %A Zhou, Jie %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 liang-etal-2022-bjtu %X This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT’22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we apply the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 81.0 and 94.6 COMET scores on English-German and German-English, respectively. The COMET scores of English-German and German-English are the highest among all submissions. %U https://aclanthology.org/2022.wmt-1.91 %P 955-961
Markdown (Informal)
[BJTU-WeChat’s Systems for the WMT22 Chat Translation Task](https://aclanthology.org/2022.wmt-1.91) (Liang et al., WMT 2022)
- BJTU-WeChat’s Systems for the WMT22 Chat Translation Task (Liang et al., WMT 2022)
ACL
- Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, and Jie Zhou. 2022. BJTU-WeChat’s Systems for the WMT22 Chat Translation Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 955–961, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.