Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation. However, conventional NAT models suffer from limited expression power and performance degradation compared to autoregressive (AT) models due to the assumption of conditional independence among target tokens. To address these limitations, we propose a novel approach called PCFG-NAT, which leverages a specially designed Probabilistic Context-Free Grammar (PCFG) to enhance the ability of NAT models to capture complex dependencies among output tokens. Experimental results on major machine translation benchmarks demonstrate that PCFG-NAT further narrows the gap in translation quality between NAT and AT models. Moreover, PCFG-NAT facilitates a deeper understanding of the generated sentences, addressing the lack of satisfactory explainability in neural machine translation.Code is publicly available at https://github.com/ictnlp/PCFG-NAT.

翻译：非自回归Transformer（NAT）显著加速了神经机器翻译的推理过程。然而，由于目标词元之间条件独立性的假设，传统NAT模型存在表达能力有限、性能较自回归（AT）模型有所下降的问题。针对这些局限性，我们提出了一种名为PCFG-NAT的新方法，该方法利用专门设计的概率上下文无关文法（PCFG）来增强NAT模型捕捉输出词元间复杂依赖关系的能力。在主要机器翻译基准上的实验结果表明，PCFG-NAT进一步缩小了NAT与AT模型在翻译质量上的差距。此外，PCFG-NAT有助于更深入地理解生成语句，解决了神经机器翻译缺乏令人满意的可解释性的问题。代码已公开于https://github.com/ictnlp/PCFG-NAT。