Future-Guided Incremental Transformer for Simultaneous Translation

Simultaneous translation is the kind of device translation, where by output is generated although studying supply sentences. It can be applied in the live subtitle or simultaneous interpretation.

Nonetheless, the latest guidelines have minimal computational pace and lack direction from foreseeable future supply info. These two weaknesses are prevail over by a a short while ago suggested strategy termed Upcoming-Guided Incremental Transformer.

It uses the regular embedding layer to summarize the consumed supply info and keep away from time-consuming recalculation. The predictive skill is enhanced by embedding some foreseeable future info through information distillation. The results demonstrate that education pace is accelerated about 28 periods in comparison to currently applied styles. Improved translation excellent was also realized on the Chinese-English and German-English simultaneous translation jobs.

Simultaneous translation (ST) starts translations synchronously although studying supply sentences, and is applied in quite a few on line scenarios. The past wait-k coverage is concise and realized fantastic results in ST. Nonetheless, wait-k coverage faces two weaknesses: minimal education pace prompted by the recalculation of concealed states and lack of foreseeable future supply info to tutorial education. For the minimal education pace, we suggest an incremental Transformer with an regular embedding layer (AEL) to accelerate the pace of calculation of the concealed states through education. For foreseeable future-guided education, we suggest a typical Transformer as the instructor of the incremental Transformer, and consider to invisibly embed some foreseeable future info in the design through information distillation. We conducted experiments on Chinese-English and German-English simultaneous translation jobs and in comparison with the wait-k coverage to examine the proposed strategy. Our strategy can correctly improve the education pace by about 28 periods on regular at different k and implicitly embed some predictive talents in the design, achieving better translation excellent than wait-k baseline.

Connection: https://arxiv.org/abs/2012.12465

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