Simultaneous machine translation (SiMT) generates translation while reading the whole source sentence. However, existing SiMT models are typically trained using the same reference disregarding the varying amounts of available source information at different latency. Training the model with ground-truth at low latency may introduce forced anticipations, whereas utilizing reference consistent with the source word order at high latency results in performance degradation. Consequently, it is crucial to train the SiMT model with appropriate reference that avoids forced anticipations during training while maintaining high quality. In this paper, we propose a novel method that provides tailored reference for the SiMT models trained at different latency by rephrasing the ground-truth. Specifically, we introduce the tailor, induced by reinforcement learning, to modify ground-truth to the tailored reference. The SiMT model is trained with the tailored reference and jointly optimized with the tailor to enhance performance. Importantly, our method is applicable to a wide range of current SiMT approaches. Experiments on three translation tasks demonstrate that our method achieves state-of-the-art performance in both fixed and adaptive policies.

翻译：同步机器翻译（SiMT）在读取整个源句子的同时生成译文。然而，现有SiMT模型通常使用相同的参考进行训练，忽略了不同延迟下可用源信息的差异。在低延迟下使用真值训练模型可能引入强制预测，而在高延迟下使用与源词序一致的参考则会导致性能下降。因此，用适当的参考训练SiMT模型至关重要，既要避免训练中的强制预测，又要保持高质量。本文提出一种新方法，通过重新表述真值为不同延迟训练的SiMT模型提供定制参考。具体而言，我们引入由强化学习驱动的定制器，将真值修改为定制参考。SiMT模型使用定制参考进行训练，并与定制器联合优化以提升性能。重要的是，该方法适用于当前主流的多种SiMT方法。在三项翻译任务上的实验表明，我们的方法在固定策略和自适应策略下均达到了最优性能。