TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task
This work addresses translation suggestion for machine translation systems, representing an incremental improvement in a specific shared task.
The paper tackles the WMT22 Translation Suggestion task by fine-tuning pre-trained models with data augmentation and filtering, achieving first place in three out of four language directions in the Naive TS task.
This paper describes the joint submission of Alibaba and Soochow University, TSMind, to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English-German and English-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-German news translation system and MBART50 for English-Chinese as our pre-trained models. Considering the task's condition of limited use of training data, we follow the data augmentation strategies proposed by WeTS to boost our TS model performance. The difference is that 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.