  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.
