Shared Latent Space by Both Languages in Non-Autoregressive Neural Machine Translation
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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, 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.
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