Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-$k$ 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 wait-$k$ 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.

翻译：同步机器翻译(SiMT)需要鲁棒的读写策略与高质量翻译模型的共同支持。传统方法要么采用固定wait-$k$策略配合独立的wait-$k$翻译模型，要么使用与翻译模型联合训练的自适应策略。本研究提出了一种更灵活的方法，通过将自适应策略模型与翻译模型解耦来实现创新。我们的动机源于观察到：独立的多元wait-$k$模型在性能上与当前最先进SiMT方法使用的自适应策略不相上下。具体而言，我们提出了基于差异性的自适应策略DaP，该策略能够根据未来信息导致的翻译分布潜在差异，为任意翻译模型做出读写决策。DaP通过轻量级参数扩展冻结的wait-$k$模型，兼具内存高效与计算高效特性。多个基准实验结果表明，我们的方法在翻译准确性与延迟之间实现了更优的权衡，显著超越了强基线模型。