Self-training Reduces Flicker in Retranslation-based Simultaneous Translation (2023)

Abstract: In simultaneous translation, the 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 show that self-training improves the flicker-latency tradeoff, while maintaining similar translation quality to the original. Our analysis indicates that self-training reduces flicker by controlling monotonicity. Furthermore, self-training can be combined with biased beam search to further improve the flicker-latency tradeoff.

## introduction simultaneous machine translation systems, which process their input word by word instead of sentence by sentence, must strike a balance between producing output immediately (and so reducing quality because of incomplete input) and waiting for further input (and so increasing latency). ideally, a good simultaneous translation system will provide a pareto-optimal tradeoff between quality and latency. a straightforward way of doing simultaneous translation is retranslation @xcite , which has the advantage that it can be used with an unmodified machine translation (mt) inference engine, and can perform better than the alternative streaming-based approaches @xcite . the disadvantage is that retranslation may change previous output causing flicker, leading to a poor user experience, and so flicker needs to be balanced with latency and quality. we argue that flickering is caused by two different (but related) issues: (i) lexical instability of the translation -the system "changes its mind" as more source is revealed, swapping one word for * work done while at the university of edinburgh. in non-autoregressive translation (nat), a related problem, known as the "multimodality" problem @xcite , has been addressed using knowledge distillation @xcite . we therefore investigate whether this can also reduce flicker in simultaneous translation. since the initial model and the distilled model have the same architecture in our work, approximating kd is essentially self-training foot_1 . we show that a selftrained model is able to achieve the same quality as the initial model, but with improved flickerlatency tradeoff. we also show that self-training @xcite ) can be combined with biased beam search to further improve the flickerlatency tradeoff. furthermore, we show experiments that link flicker to monotonicity. ## conclusion we show that self-training reduces the flicker in retranslation-based simultaneous translation, whilst retaining quality. our experiments link this flicker reduction to increased monotonicity and reduced entropy of the self-trained model. although biased beam search can obtain larger reductions in flicker, it requires more careful parameter tuning, and a modified inference engine.