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Non-autoregressive Transformer(NAT) significantly accelerates the inferenceof neural machine translation. However, conventional NAT models suffer fromlimited expression power and performance degradation compared to autoregressive(AT) models due to the assumption of conditional independence among targettokens. To address these limitations, we propose a novel approach calledPCFG-NAT, which leverages a specially designed Probabilistic Context-FreeGrammar (PCFG) to enhance the ability of NAT models to capture complexdependencies among output tokens. Experimental results on major machinetranslation benchmarks demonstrate that PCFG-NAT further narrows the gap intranslation quality between NAT and AT models. Moreover, PCFG-NAT facilitates adeeper understanding of the generated sentences, addressing the lack ofsatisfactory explainability in neural machine translation.Code is publiclyavailable at https://github.com/ictnlp/PCFG-NAT.

Non-autoregressive Machine Translation with Probabilistic Context-free Grammar