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We present a novel approach for structured data-to-text generation thataddresses the limitations of existing methods that primarily focus on specifictypes of structured data. Our proposed method aims to improve performance inmulti-task training, zero-shot and few-shot scenarios by providing a unifiedrepresentation that can handle various forms of structured data such as tables,knowledge graph triples, and meaning representations. We demonstrate that ourproposed approach can effectively adapt to new structured forms, and canimprove performance in comparison to current methods. For example, our methodresulted in a 66% improvement in zero-shot BLEU scores when transferring modelstrained on table inputs to a knowledge graph dataset. Our proposed method is animportant step towards a more general data-to-text generation framework.

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning