DART: Open-Domain Structured Data Record to Text Generation
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 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 https://github.com/Yale-LILY/dart.
Keywords
Cite
@article{arxiv.2007.02871,
title = {DART: Open-Domain Structured Data Record to Text Generation},
author = {Linyong Nan and Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Mutethia Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher and Nazneen Fatema Rajani},
journal= {arXiv preprint arXiv:2007.02871},
year = {2021}
}
Comments
NAACL 2021