R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework
  for Low-Latency Simultaneous Speech Translation
Jiaxin Guo Zhanglin Wu Zongyao Li Hengchao Shang Daimeng Wei Xiaoyu Chen Zhiqiang Rao Shaojun Li Hao Yang
Incremental Decoding is an effective framework that enables the use of an
offline model in a simultaneous setting without modifying the original model,
making it suitable for Low-Latency Simultaneous Speech Translation. However,
this framework may introduce errors when the system outputs from incomplete
input. To reduce these output errors, several strategies such as Hold-$n$,
LA-$n$, and SP-$n$ can be employed, but the hyper-parameter $n$ needs to be
carefully selected for optimal performance. Moreover, these strategies are more
suitable for end-to-end systems than cascade systems. In our paper, we propose
a new adaptable and efficient policy named "Regularized Batched Inputs". Our
method stands out by enhancing input diversity to mitigate output errors. We
suggest particular regularization techniques for both end-to-end and cascade
systems. We conducted experiments on IWSLT Simultaneous Speech Translation
(SimulST) tasks, which demonstrate that our approach achieves low latency while
maintaining no more than 2 BLEU points loss compared to offline systems.
Furthermore, our SimulST systems attained several new state-of-the-art results
in various language directions.