BIT-Xiaomi’s System for AutoSimTrans 2022
Mengge Liu | Xiang Li | Bao Chen | Yanzhi Tian | Tianwei Lan | Silin Li | Yuhang Guo | Jian Luan | Bin Wang
Proceedings of the Third Workshop on Automatic Simultaneous Translation
This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track and the En-Es test-to-text track. In our system, wait-k is employed to train prefix-to-prefix translation models. We integrate streaming chunking to detect boundaries as the source streaming read in. We further improve our system with data selection, data-augmentation and R-drop training methods. Results show that our wait-k implementation outperforms organizer’s baseline by 8 BLEU score at most, and our proposed streaming chunking method further improves about 2 BLEU in low latency regime.