Dap-SiMT: divergence-based adaptive policy for simultaneous machine translation

Abstract

In the realm of Simultaneous Machine Translation (SiMT), a robust read/write (R/W) policy is essential alongside a high-quality translation model. Traditional methods typically employ either a fixed wait-k policy in sync with a wait-k translation model or an adaptive policy that is co-developed with a dedicated translation model. This study introduces a more versatile approach by decoupling the adaptive policy from the translation model. Our rationale is based on the finding that an independent multi-path wait-k model, when combined with adaptive policies utilized in advanced SiMT systems, can perform competitively. Specifically, we present DaP, a divergence-based adaptive policy, which dynamically adjusts read/write decisions for any translation model, taking into account potential divergence in translation distributions resulting from future information. Extensive experiments across multiple benchmarks reveal that our method significantly enhances the balance between translation accuracy and latency, surpassing strong baselines.