Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information

Understand

Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration.

• However, existing NAT models still have a big gap in translation quality compared to autoregressive neural machine translation models due to the enormous decoding space.
• To address this problem, we propose a novel NAT framework named ReorderNAT which explicitly models the reordering information in the decoding procedure.
• We further introduce deterministic and non-deterministic decoding strategies that utilize reordering information to narrow the decoding search space in our proposed ReorderNAT.