Information-Transport-based Policy for Simultaneous Translation
This work addresses the policy decision problem in simultaneous translation for real-time language processing applications, representing a novel method for a known bottleneck.
The paper tackles the challenge of determining when to translate or wait in simultaneous translation by proposing an Information-Transport-based Simultaneous Translation (ITST) method, which quantifies information transport from source to target tokens and achieves state-of-the-art performance on text-to-text and speech-to-text tasks.
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.