Data-to-text generation involves transforming structured data, often
represented as predicate-argument tuples, into coherent textual descriptions.
Despite recent advances, systems still struggle when confronted with unseen
combinations of predicates, producing unfaithful descriptions (e.g.
hallucinations or omissions). We refer to this issue as compositional
generalisation, and it encouraged us to create a benchmark for assessing the
performance of different approaches on this specific problem. Furthermore, we
propose a novel model that addresses compositional generalization by clustering
predicates into groups. Our model generates text in a sentence-by-sentence
manner, relying on one cluster of predicates at a time. This approach
significantly outperforms T5~baselines across all evaluation metrics.Notably,
it achieved a 31% improvement over T5 in terms of a metric focused on
maintaining faithfulness to the input.

数据到文本生成涉及将结构化数据转化为连贯的文本描述，本论文提出了一种解决复合概括问题的新模型，通过将谓词聚类成组，在生成文本时逐句依赖于一组谓词，显著优于基线模型 T5 的各种评估指标，尤其在维护对输入的忠实度指标上提升了 31%。