Adaptive Policy with Wait-k Model for Simultaneous Translation
Libo Zhao, Kai C. Fan, Wei Ping Luo, Wu Jing, Shushu Wang, Ziqian Zeng, Zhongqiang Huang · 2023
Simultaneous machine translation (SiMT) requires a robust read/write (R/W) policy in conjunction with a high-quality translation model.Traditional methods rely on either a fixed waitk policy coupled with a standalone wait-k translation model, or an adaptive policy jointly trained with the translation model.In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model.Our motivation stems from the observation that a standalone multi-path waitk model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches.Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information.DaP extends a frozen wait-k model with lightweight parameters, and is both memory and computation efficient.Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.1