LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training. (arXiv:2112.01404v3 [cs.CL] UPDATED)

Natural language generation from structured data mainly focuses on
surface-level descriptions, suffering from uncontrollable content selection and
low fidelity. Previous works leverage logical forms to facilitate logical
knowledge-conditioned text generation. Though achieving remarkable progress,
they are data-hungry, which makes the adoption for real-world applications
challenging with limited data. To this end, this paper proposes a unified
framework for logical knowledge-conditioned text generation in the few-shot
setting. With only a few seeds logical forms (e.g., 20/100 shot), our approach
leverages self-training …

adoption applications arxiv data fidelity knowledge language language generation low natural natural language natural language generation progress self-training structured data text text generation training world