Few-Shot Data-to-Text Generation via Unified Representation and
  Multi-Source Learning
Alexander Hanbo Li Mingyue Shang Evangelia Spiliopoulou Jie Ma Patrick Ng Zhiguo Wang Bonan Min William Wang Kathleen McKeown Vittorio Castelli Dan Roth Bing Xiang
We present a novel approach for structured data-to-text generation that
addresses the limitations of existing methods that primarily focus on specific
types of structured data. Our proposed method aims to improve performance in
multi-task training, zero-shot and few-shot scenarios by providing a unified
representation that can handle various forms of structured data such as tables,
knowledge graph triples, and meaning representations. We demonstrate that our
proposed approach can effectively adapt to new structured forms, and can
improve performance in comparison to current methods. For example, our method
resulted in a 66% improvement in zero-shot BLEU scores when transferring models
trained on table inputs to a knowledge graph dataset. Our proposed method is an
important step towards a more general data-to-text generation framework.