Self-training Reduces Flicker in Retranslation-based Simultaneous Translation

Anonymous

Keywords: Simultaneous translation, machine translation

Abstract: In simultaneous translation, the \emph{retranslation} approach has the advantage of requiring no modifications to the inference engine. However, in order to reduce the undesirable flicker in the output, previous work has resorted to increasing the latency through masking, and introducing specialised inference, thus losing the simplicity of the approach. In this work, we will show that self-training improves the flicker latency tradeoff, whilst maintaining similar translation quality to the original. Our analysis indicates that self-training reduces flicker by controlling monotonicity. Self-training can be combined with biased beam search to further improve the flicker-latency tradeoff.

Paper Type: short

Research Area: Machine Translation