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


Abstract
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.
Anthology ID:
2022.autosimtrans-1.6
Volume:
Proceedings of the Third Workshop on Automatic Simultaneous Translation
Month:
July
Year:
2022
Address:
Online
Venue:
AutoSimTrans
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
34–42
Language:
URL:
https://aclanthology.org/2022.autosimtrans-1.6
DOI:
10.18653/v1/2022.autosimtrans-1.6
Bibkey:
Cite (ACL):
Mengge Liu, Xiang Li, Bao Chen, Yanzhi Tian, Tianwei Lan, Silin Li, Yuhang Guo, Jian Luan, and Bin Wang. 2022. BIT-Xiaomi’s System for AutoSimTrans 2022. In Proceedings of the Third Workshop on Automatic Simultaneous Translation, pages 34–42, Online. Association for Computational Linguistics.
Cite (Informal):
BIT-Xiaomi’s System for AutoSimTrans 2022 (Liu et al., AutoSimTrans 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.autosimtrans-1.6.pdf
Data
BSTC