  Simultaneous translation (ST) outputs translation while receiving the source
inputs, and hence requires a policy to determine whether to translate a target
token or wait for the next source token. The major challenge of ST is that each
target token can only be translated based on the current received source
tokens, where the received source information will directly affect the
translation quality. So naturally, how much source information is received for
the translation of the current target token is supposed to be the pivotal
evidence for the ST policy to decide between translating and waiting. In this
paper, we treat the translation as information transport from source to target
and accordingly propose an Information-Transport-based Simultaneous Translation
(ITST). ITST quantifies the transported information weight from each source
token to the current target token, and then decides whether to translate the
target token according to its accumulated received information. Experiments on
both text-to-text ST and speech-to-text ST (a.k.a., streaming speech
translation) tasks show that ITST outperforms strong baselines and achieves
state-of-the-art performance.
