Existing neural methods have shown great potentials towards generating
informative text from structured tabular data as well as maintaining high
content fidelity. However, few of them shed light on generating personalized
expressions, which often requires well-aligned persona-table-text datasets that
are difficult to obtain. To overcome these obstacles, we explore personalized
table-to-text generation under a zero-shot setting, by assuming no well-aligned
persona-table-text triples are required during training. To this end, we
firstly collect a set of unpaired persona information and then propose a
semi-supervised approach with contrastive persona distillation (S2P-CPD) to
generate personalized context. Specifically, tabular data and persona
information are firstly represented as latent variables separately. Then, we
devise a latent space fusion technique to distill persona information into the
table representation. Besides, a contrastive-based discriminator is employed to
guarantee the style consistency between the generated context and its
corresponding persona. Experimental results on two benchmarks demonstrate
S2P-CPD's ability on keeping both content fidelity and personalized
expressions.

研究采用零样本的方式进行个性化表格文本生成，提出了一种半监督方法，利用对比个性蒸馏实现个性化内容。该方法旨在提高文本信息生成的个性化表现，同时维持内容的真实性，实验表明该方法表现出色。