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Existing neural methods have shown great potentials towards generatinginformative text from structured tabular data as well as maintaining highcontent fidelity. However, few of them shed light on generating personalizedexpressions, which often requires well-aligned persona-table-text datasets thatare difficult to obtain. To overcome these obstacles, we explore personalizedtable-to-text generation under a zero-shot setting, by assuming no well-alignedpersona-table-text triples are required during training. To this end, wefirstly collect a set of unpaired persona information and then propose asemi-supervised approach with contrastive persona distillation (S2P-CPD) togenerate personalized context. Specifically, tabular data and personainformation are firstly represented as latent variables separately. Then, wedevise a latent space fusion technique to distill persona information into thetable representation. Besides, a contrastive-based discriminator is employed toguarantee the style consistency between the generated context and itscorresponding persona. Experimental results on two benchmarks demonstrateS2P-CPD's ability on keeping both content fidelity and personalizedexpressions.

Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation