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 URL
We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization . Our data and code can be found at URL
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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
present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data
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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
contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new
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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
results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2)
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on the WebNLG 2017 dataset. DART is available
. Our data and code can be found