The AISP-SJTU Translation System for WMT 2022
Guangfeng Liu, Qinpei Zhu, Xingyu Chen, Renjie Feng, Jianxin Ren, Renshou Wu, Qingliang Miao, Rui Wang, Kai Yu
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)
- Venue:
- WMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 310–317
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.24
- DOI:
- 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. In Proceedings 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wmt-1.24.pdf