This paper explores deep latent variable models for semi-supervised
paraphrase generation, where the missing target pair is modelled as a latent
paraphrase sequence. We present a novel unsupervised model named variational
sequence auto-encoding reconstruction (VSAR), which performs latent sequence
inference given an observed text. To leverage information from text pairs, we
introduce a supervised model named dual directional learning (DDL). Combining
VSAR with DDL (DDL+VSAR) enables us to conduct semi-supervised learning;
however, the combined model suffers from a cold-start problem. To combat this
issue, we propose to deal with better weight initialisation, leading to a
two-stage training scheme named knowledge reinforced training. Our empirical
evaluations suggest that the combined model yields competitive performance
against the state-of-the-art supervised baselines on complete data.
Furthermore, in scenarios where only a fraction of the labelled pairs are
available, our combined model consistently outperforms the strong supervised
model baseline (DDL and Transformer) by a significant margin.

研究利用深度潜在变量模型进行半监督任务，提出了 VSAR 和 DDL 两个模型，组合使用可提高性能，在已知文本对的情况下使用 DDL+VSAR 进行半监督学习，使用提出的权重初始化方法解决冷启动问题，经实验验证，该模型在数据不完整的情况下具有很好的性能。