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    <abstract>This paper describes the joint submission of Alibaba and Soochow University to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English to/from German and English to/from Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR’s WMT19 English to/from German news translation system and MBART50 for English to/from Chinese as our pre-trained models. Considering the task’s condition of limited use of training data, we follow the data augmentation strategies provided by Yang to boost our TS model performance. And we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.</abstract>
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%0 Conference Proceedings
%T TSMind: Alibaba and Soochow University’s Submission to the WMT22 Translation Suggestion Task
%A Ge, Xin
%A Wang, Ke
%A Wang, Jiayi
%A Xiao, Nini
%A Duan, Xiangyu
%A Zhao, Yu
%A Zhang, Yuqi
%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 ge-etal-2022-tsmind
%X This paper describes the joint submission of Alibaba and Soochow University to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English to/from German and English to/from Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR’s WMT19 English to/from German news translation system and MBART50 for English to/from Chinese as our pre-trained models. Considering the task’s condition of limited use of training data, we follow the data augmentation strategies provided by Yang to boost our TS model performance. And we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.
%U https://aclanthology.org/2022.wmt-1.123
%P 1198-1204

Markdown (Informal)

[TSMind: Alibaba and Soochow University’s Submission to the WMT22 Translation Suggestion Task](https://aclanthology.org/2022.wmt-1.123) (Ge et al., WMT 2022)

• TSMind: Alibaba and Soochow University’s Submission to the WMT22 Translation Suggestion Task (Ge et al., WMT 2022)

ACL

• Xin Ge, Ke Wang, Jiayi Wang, Nini Xiao, Xiangyu Duan, Yu Zhao, and Yuqi Zhang. 2022. TSMind: Alibaba and Soochow University’s Submission to the WMT22 Translation Suggestion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1198–1204, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.