论文标题
转向自适应：将后评估整合到同时的机器翻译中
Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation
论文作者
论文摘要
同时机器翻译（SIMT）在阅读整个源句子之前开始翻译，并采用固定或自适应策略来生成目标句子。与固定政策相比，自适应政策通过采用灵活的翻译政策实现了更好的延迟质量折衷。如果该政策在采取行动之前可以评估合理性，则行动不正确的可能性也将减少。但是，以前的方法在采取行动之前缺乏评估。在本文中，我们提出了一种通过将后评估整合到固定策略中来执行自适应政策的方法。具体来说，每当生成候选令牌时，我们的模型都会通过测量源内容的变化来评估下一个动作的合理性。然后，我们的模型将根据评估结果采取不同的措施。在三个翻译任务上进行的实验表明，在所有延迟下，我们的方法可以超过强基础。
Simultaneous machine translation (SiMT) starts its translation before reading the whole source sentence and employs either fixed or adaptive policy to generate the target sentence. Compared to the fixed policy, the adaptive policy achieves better latency-quality tradeoffs by adopting a flexible translation policy. If the policy can evaluate rationality before taking action, the probability of incorrect actions will also decrease. However, previous methods lack evaluation of actions before taking them. In this paper, we propose a method of performing the adaptive policy via integrating post-evaluation into the fixed policy. Specifically, whenever a candidate token is generated, our model will evaluate the rationality of the next action by measuring the change in the source content. Our model will then take different actions based on the evaluation results. Experiments on three translation tasks show that our method can exceed strong baselines under all latency.
