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 · 2022
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 utilized to train prefix-to-prefix translation models.We integrate streaming chunking to detect segmentation boundaries as the source streaming reading in.We further improve our system with data selection, data augmentation, and R-Drop training methods.Results show that our wait-k implementation outperforms the organizer's baseline by at most 8 BLEU score and our proposed streaming chunking method further improves by about 2 BLEU score in the low latency regime.