Latent variable modeling in non-autoregressive neural machine translation (NAT) is a promising approach to mitigate the multimodality problem. In the previous works, they added an auxiliary model to estimate the posterior distribution of the latent variable conditioned on the source and target sentences. However, it causes several disadvantages, such as redundant information extraction in the latent variable, increasing parameters, and a tendency to ignore a part of the information from the inputs. In this paper, we propose a new latent variable modeling that is based on a dual reconstruction perspective and an advanced hierarchical latent modeling approach. Our proposed method, {\em LadderNMT}, shares a latent space across both languages so that it hypothetically alleviates or solves the above disadvantages. Experimental results quantitatively and qualitatively demonstrate that our proposed latent variable modeling learns an advantageous latent space and significantly improves translation quality in WMT translation tasks.

本研究提出一种新的潜变量建模方法，名为LadderNMT，并通过在非自回归神经机器翻译中的实验结果展示了其在跨语言翻译任务中显著提高翻译质量的优势。该方法基于双重重建视角和先进的分层潜变量建模方法，共享两种语言的潜空间，以减轻或解决了以前方法中存在的多模态问题，参数过多，无法获取全部输入信息的缺点。

非自回归神经机器翻译中两种语言共享的潜在空间