In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for conditional natural language generation with none or a handful of task-specific labeled examples. In order to improve compositional generalization, our model performs disentangled representation learning by introducing a prior for the latent content space and another prior for the latent label space. We show both empirically and theoretically that the conditional priors can already disentangle representations even without specific regularizations as in the prior work. We can also sample diverse content representations from the content space without accessing data of the seen tasks, and fuse them with the representations of novel tasks for generating diverse texts in the low-resource settings. Our extensive experiments demonstrate the superior performance of our model over competitive baselines in terms of i) data augmentation in continuous zero/few-shot learning, and ii) text style transfer in both zero/few-shot settings.

本文提出了一种具有解缩先验的变分自编码器 VAE-DPRIOR，用于无或很少任务特定标记示例的特定任务自然语言生成。我们通过为隐藏内容空间引入条件先验和为隐藏标签空间引入另一个条件先验来执行分解表示学习，以应对跨任务的组合泛化，并展示了这些新型先验即使在先前的工作中没有特定正则化也能够分解表示。内在内容先验使得从训练过的任务的内容空间中直接采样多样化内容表示成为可能，并将它们与新任务的表示融合，以在低资源环境下生成语义多样化的文本。我们的广泛实验展示了我们的模型在连续零/少样本学习中数据增强和在少样本设置中文本风格转移方面的卓越性能。

低资源任务特定自然语言生成的解缠自编码器