Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data
This work compares data-driven knowledge distillation (DDKD) against zero-shot inference and fine-tuning on out-of-domain D2T data, and introduces structure-preserving augmentation via structural subsampling and perturbation in cross-domain D2T generation.
Yifei Song, Kun Efimov-Zhang, Claire Gardent
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