Semi-Autoregressive Neural Machine Translation

Understand

Existing approaches to neural machine translation are typically autoregressive models.

• While these models attain state-of-the-art translation quality, they are suffering from low parallelizability and thus slow at decoding long sequences.
• In this paper, we propose a novel model for fast sequence generation --- the semi-autoregressive Transformer (SAT).
• The SAT keeps the autoregressive property in global but relieves in local and thus is able to produce multiple successive words in parallel at each time step.