  Simultaneous translation, which starts translating each sentence after
receiving only a few words in source sentence, has a vital role in many
scenarios. Although the previous prefix-to-prefix framework is considered
suitable for simultaneous translation and achieves good performance, it still
has two inevitable drawbacks: the high computational resource costs caused by
the need to train a separate model for each latency $k$ and the insufficient
ability to encode information because each target token can only attend to a
specific source prefix. We propose a novel framework that adopts a simple but
effective decoding strategy which is designed for full-sentence models. Within
this framework, training a single full-sentence model can achieve arbitrary
given latency and save computational resources. Besides, with the competence of
the full-sentence model to encode the whole sentence, our decoding strategy can
enhance the information maintained in the decoded states in real time.
Experimental results show that our method achieves better translation quality
than baselines on 4 directions: Zh$\rightarrow$En, En$\rightarrow$Ro and
En$\leftrightarrow$De.
