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.
翻译:同时的机器翻译(SimMT) 在阅读整个源句之前开始翻译, 并采用固定或适应政策来生成目标句子。 与固定政策相比, 适应性政策通过采用灵活的翻译政策实现了更好的长期质量权衡。 如果该政策能够在采取行动之前评估合理性, 不正确行动的概率也会降低。 但是, 先前的方法在采取行动之前缺乏对行动的评价。 在本文件中, 我们建议了一种方法, 通过将事后评估纳入固定政策来实施适应性政策。 具体地说, 只要产生了候选标牌, 我们的模式将评估下一个行动的合理性, 通过测量源内容的变化。 我们的模型将根据评估结果采取不同的行动。 对三项翻译任务的实验表明, 我们的方法可以超过所有时间下的强势基线。