Semi-Autoregressive Neural Machine Translation
Chunqi Wang, Ji Zhang, Haiqing Chen · 2018
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 generationthe semi-autoregressive Transformer (SAT).The SAT keeps the autoregressive property in global but relieves in local and thus are able to produce multiple successive words in parallel at each time step.Experiments conducted on English-German and Chinese-English translation tasks show that the SAT achieves a good balance between translation quality and decoding speed.On WMT'14 English-German translation, the SAT achieves 5.58× speedup while maintaining 88% translation quality, significantly better than the previous non-autoregressive methods.When produces two words at each time step, the SAT is almost lossless (only 1% degeneration in BLEU score).