TheAISP-SJTU Translation System forWMT 2022
Guangfeng Liu,Qinpei Zhu,Xingyu Chen,Renjie Feng,Jianxin Ren,Renshou Wu,Qingliang Miao,Rui Wang,Kai Yu
Correct Metadata for
Abstract
This paper describes AISP-SJTU’s participation in WMT 2022 shared general MT task. In this shared task, we participated in four translation directions: English-Chinese, Chinese-English, English-Japanese and Japanese-English. Our systems are based on the Transformer architecture with several novel and effective variants, including network depth and internal structure. In our experiments, we employ data filtering, large-scale back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge finetune and model ensemble. The constrained systems achieve 48.8, 29.7, 39.3 and 22.0 case-sensitive BLEU scores on EN-ZH, ZH-EN, EN-JA and JA-EN, respectively.- Anthology ID:
- 2022.wmt-1.24
- 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:
- 310–317
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.24/
- DOI:
- Bibkey:
- Cite (ACL):
- Guangfeng Liu, Qinpei Zhu, Xingyu Chen, Renjie Feng, Jianxin Ren, Renshou Wu, Qingliang Miao, Rui Wang, and Kai Yu. 2022.The AISP-SJTU Translation System for WMT 2022. InProceedings of the Seventh Conference on Machine Translation (WMT), pages 310–317, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- The AISP-SJTU Translation System for WMT 2022 (Liu et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.24.pdf
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@inproceedings{liu-etal-2022-aisp, title = "The {AISP}-{SJTU} Translation System for {WMT} 2022", author = "Liu, Guangfeng and Zhu, Qinpei and Chen, Xingyu and Feng, Renjie and Ren, Jianxin and Wu, Renshou and Miao, Qingliang and Wang, Rui and Yu, Kai", 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-1.24/", pages = "310--317", abstract = "This paper describes AISP-SJTU`s participation in WMT 2022 shared general MT task. In this shared task, we participated in four translation directions: English-Chinese, Chinese-English, English-Japanese and Japanese-English. Our systems are based on the Transformer architecture with several novel and effective variants, including network depth and internal structure. In our experiments, we employ data filtering, large-scale back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge finetune and model ensemble. 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%0 Conference Proceedings%T The AISP-SJTU Translation System for WMT 2022%A Liu, Guangfeng%A Zhu, Qinpei%A Chen, Xingyu%A Feng, Renjie%A Ren, Jianxin%A Wu, Renshou%A Miao, Qingliang%A Wang, Rui%A Yu, Kai%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 liu-etal-2022-aisp%X This paper describes AISP-SJTU‘s participation in WMT 2022 shared general MT task. In this shared task, we participated in four translation directions: English-Chinese, Chinese-English, English-Japanese and Japanese-English. Our systems are based on the Transformer architecture with several novel and effective variants, including network depth and internal structure. In our experiments, we employ data filtering, large-scale back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge finetune and model ensemble. The constrained systems achieve 48.8, 29.7, 39.3 and 22.0 case-sensitive BLEU scores on EN-ZH, ZH-EN, EN-JA and JA-EN, respectively.%U https://aclanthology.org/2022.wmt-1.24/%P 310-317
Markdown (Informal)
[The AISP-SJTU Translation System for WMT 2022](https://aclanthology.org/2022.wmt-1.24/) (Liu et al., WMT 2022)
- The AISP-SJTU Translation System for WMT 2022 (Liu et al., WMT 2022)
ACL
- Guangfeng Liu, Qinpei Zhu, Xingyu Chen, Renjie Feng, Jianxin Ren, Renshou Wu, Qingliang Miao, Rui Wang, and Kai Yu. 2022.The AISP-SJTU Translation System for WMT 2022. InProceedings of the Seventh Conference on Machine Translation (WMT), pages 310–317, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.