Semi-supervised learning algorithms in neural machine translation (NMT) have
significantly improved translation quality compared to the supervised learning
methods by using additional monolingual corpora. Among them, back-translation
is a theoretically well-structured and cutting-edge method. Given two
pre-trained NMT models between source and target languages, one NMT model
translates a monolingual sentence to a latent sentence, and the other
reconstructs the monolingual input sentence given the latent sentence. Based on
this auto-encoding framework, previous work tried to apply the variational
auto-encoder's (VAE) training framework to the back-translation. However, the
discrete property of the latent sentence made it impossible to use
backpropagation in the end-to-end fashion. In this paper, we propose a {\it
categorical reparameterization trick} that makes NMT models generate {\it
differentiable sentences}. Based on the proposed method, end-to-end learning is
possible so that two NMT models for the back-translation can be trained as a
unified model. In addition, we propose several regularization techniques that
are especially advantageous to this framework. Our experiments demonstrate that
our method can achieve better BLEU scores than the previous baseline, on the
datasets of the WMT18 translation task.

本篇论文提出了一种基于交叉学习的半监督学习算法，其中使用了背译和变分自编码器以及分类重参技巧来提高机器翻译的质量。