The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Bao Guo, Mengge Liu, Wen Zhang, Hexuan Chen, Chang Mu, Xiang Li, Jianwei Cui, Bin Wang, Yuhang Guo · 2022
This system paper describes the Xiaomi Translation System for the IWSLT 2022 Simultaneous Speech Translation (noted as SST) shared task.We participate in the Englishto-Mandarin Chinese Text-to-Text (noted as T2T) track.Our system is built based on the Transformer model with novel techniques borrowed from our recent research work.For the data filtering, language-model-based and rule-based methods are conducted to filter the data to obtain high-quality bilingual parallel corpora.We also strengthen our system with some dominating techniques related to data augmentation, such as knowledge distillation, tagged back-translation, and iterative backtranslation.We also incorporate novel training techniques such as R-drop, deep model, and large batch training which have been shown to be beneficial to the naive Transformer model.In the SST scenario, several variations of wait-k strategies are explored.Furthermore, in terms of robustness, both data-based and model-based ways are used to reduce the sensitivity of our system to Automatic Speech Recognition (ASR) outputs.We finally design some inference algorithms and use the adaptive-ensemble method based on multiple model variants to further improve the performance of the system.Compared with strong baselines, fusing all techniques can improve our system by 2~3 BLEU scores under different latency regimes.