DART : Open-Domain Structured Data Record to Text Generation (2021)

Abstract: 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 spoken dialogue systems by utilizing techniques including 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 https://github.com/Yale-LILY/dart .

## introduction automatically generating textual descriptions from structured data improves the accessibility of knowledge bases to lay users. such applications include explaining data records to non-experts @xcite , writing sports news @xcite , summarizing information in multiple documents @xcite , and generating dialogue responses @xcite . while significant progress has been made in this field, there are still several issues with existing data-to-text datasets. first, they adopt a flat ontology structure of the data, such as slot-value pairs for data records @xcite or flat schema for tables @xcite ., * now at facebook ai. to address some of these issues and to encourage further research in natural language generation from structured data, we introduce dart, a large and open-domain structured data-record-to-text generation corpus. the goal of dart is to harvest the diverse predicates occurred in wikipedia tables, which is significantly richer than those defined in the domain specific ontologies e2e and webnlg were built on ( our contributions are as follows. (1) we present a large and open-domain corpus for structured data record to text generation, annotated with tree ontologies converted from the table. this hierarchical input differentiates our corpus from existing datato-text corpora. (2) we benchmark several stateof-the-art data-to-text models to show that dart introduces new generalization challenges. (3) we demonstrate that using dart for data augmentation improves the performance of existing models on the webnlg 2017 dataset. we expect the results to generalize to other data-to-text datasets given the open-domain nature of dart. ## dart data collection as shown in figure 1 , dart is constructed from three different sources: (1) human annotation on wikipedia tables from two table semantic parsing and question answering datasets wikisql and wik-itablequestions ( § 2.1), (2) automatic conversion of questions in wikisql to declarative sentences ( § 2.2), and (3) incorporation of existing datasets including webnlg 2017 and cleaned e2e ( § 2.3). after collecting the triple-set, sentence pairs from various data sources, we manually canonicalized the predicates and show that dart covers a broad range of topics ( § 2.4). finally, we discuss the data split in § 2.5. ## experimental results we conduct experiments on dart and the webnlg 2017 dataset, with an ablation study on webnlg to show the benefits of using dart for data augmentation. ## human evaluation in the results in ## conclusion in this paper, we introduce dart, an open-domain corpus for structured data record to text generation. dart's ontology-preserving representation of data inputs differentiates itself from other open-domain data-to-text corpora. we found that dart introduces new challenges to several state-of-the-art data-to-text models due to its open-domain nature and its ontology structure of the semantic triple input. furthermore, we found that using it for data augmentation improves other data-to-text tasks. for future work, we will explore more controlled, high-fidelity generation that better incorporates the ontology hierarchy of data.