论文标题
差异神经机器翻译和标准化流量
Variational Neural Machine Translation with Normalizing Flows
论文作者
论文摘要
变分神经机转换（VNMT）是建模目标翻译的产生的有吸引力的框架，不仅在源句子上，而且以某些潜在的随机变量为条件。潜在变量建模可能会引入有用的统计依赖性，以提高翻译精度。不幸的是，学习内容丰富的潜在变量是非平凡的，因为潜在空间可能很大，并且在培训时，许多翻译模型都容易被许多翻译模型忽略。先前的作品对潜在代码的分布施加了强有力的假设，并限制了NMT体系结构的选择。在本文中，我们建议将VNMT框架应用于最先进的变压器，并基于归一化的流动引入更灵活的后端。我们证明了我们在内域和室外条件下提案的功效，显着优于强质基础。
Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables. The latent variable modeling may introduce useful statistical dependencies that can improve translation accuracy. Unfortunately, learning informative latent variables is non-trivial, as the latent space can be prohibitively large, and the latent codes are prone to be ignored by many translation models at training time. Previous works impose strong assumptions on the distribution of the latent code and limit the choice of the NMT architecture. In this paper, we propose to apply the VNMT framework to the state-of-the-art Transformer and introduce a more flexible approximate posterior based on normalizing flows. We demonstrate the efficacy of our proposal under both in-domain and out-of-domain conditions, significantly outperforming strong baselines.
