  Simultaneous machine translation (SimulMT) speeds up the translation process
by starting to translate before the source sentence is completely available. It
is difficult due to limited context and word order difference between
languages. Existing methods increase latency or introduce adaptive read-write
policies for SimulMT models to handle local reordering and improve translation
quality. However, the long-distance reordering would make the SimulMT models
learn translation mistakenly. Specifically, the model may be forced to predict
target tokens when the corresponding source tokens have not been read. This
leads to aggressive anticipation during inference, resulting in the
hallucination phenomenon. To mitigate this problem, we propose a new framework
that decompose the translation process into the monotonic translation step and
the reordering step, and we model the latter by the auxiliary sorting network
(ASN). The ASN rearranges the hidden states to match the order in the target
language, so that the SimulMT model could learn to translate more reasonably.
The entire model is optimized end-to-end and does not rely on external aligners
or data. During inference, ASN is removed to achieve streaming. Experiments
show the proposed framework could outperform previous methods with less
latency.
