Hybrid-Regressive Neural Machine Translation

Understand

In this work, we empirically confirm that non-autoregressive translation with an iterative refinement mechanism (IR-NAT) suffers from poor acceleration robustness because it is more sensitive to decoding batch size and computing device setting than autoregressive translation (AT).

• Inspired by it, we attempt to investigate how to combine the strengths of autoregressive and non-autoregressive translation paradigms better.
• To this end, we demonstrate through synthetic experiments that prompting a small number of AT's predictions can promote one-shot non-autoregressive translation to achieve the equivalent performance of IR-NAT.
• Following this line, we propose a new two-stage translation prototype called hybrid-regressive translation (HRT).