We introduce DART, a large dataset for open-domain structured data record to text generation. We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. This hierarchical, structured format with its open-domain nature differentiates DART from other existing table-to-text corpora. We conduct an analysis of DART on several state-of-the-art text generation models, showing that it introduces new and interesting challenges compared to existing datasets. Furthermore, we demonstrate that finetuning pretrained language models on DART facilitates out-of-domain generalization on the WebNLG 2017 dataset. DART is available at https://github.com/Yale-LILY/dart.

本文提出了DART数据集，其中包含超过82k条开放领域结构化数据到文本生成实例。我们通过从表中提取语义三元组的方式，以及利用标头之间的语义依赖关系和表标题来编码表的结构，提出了一种数据到文本生成的注释程序。我们还将从开放领域语义解析和对话行为意义表示任务中提取的异构源有效地合并，以及提出了数据集构建框架，利用树本体注释、问答对到陈述句的转换和谓词统一等技术进行最少的后期编辑。我们在DART上进行了系统评估，并展示了WebNLG 2017的最新成果，证明DART（1）对现有数据到文本数据集提出了新的挑战，（2）促进了跨领域的泛化。

DART: 开放域结构化数据记录到文本生成