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.