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Natural language generation from structured data mainly focuses onsurface-level descriptions, suffering from uncontrollable content selection andlow fidelity. Previous works leverage logical forms to facilitate logicalknowledge-conditioned text generation. Though achieving remarkable progress,they are data-hungry, which makes the adoption for real-world applicationschallenging with limited data. To this end, this paper proposes a unifiedframework for logical knowledge-conditioned text generation in the few-shotsetting. With only a few seeds logical forms (e.g., 20/100 shot), our approachleverages self-training and samples pseudo logical forms based on content andstructure consistency. Experimental results demonstrate that our approach canobtain better few-shot performance than baselines.

LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training