import csv import json import os import datasets _CITATION = """\ @inproceedings{nan-etal-2021-dart, title = "{DART}: Open-Domain Structured Data Record to Text Generation", author = "Nan, Linyong and Radev, Dragomir and Zhang, Rui and Rau, Amrit and Sivaprasad, Abhinand and Hsieh, Chiachun and Tang, Xiangru and Vyas, Aadit and Verma, Neha and Krishna, Pranav and Liu, Yangxiaokang and Irwanto, Nadia and Pan, Jessica and Rahman, Faiaz and Zaidi, Ahmad and Mutuma, Mutethia and Tarabar, Yasin and Gupta, Ankit and Yu, Tao and Tan, Yi Chern and Lin, Xi Victoria and Xiong, Caiming and Socher, Richard and Rajani, Nazneen Fatema", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.37", doi = "10.18653/v1/2021.naacl-main.37", pages = "432--447", 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.", } """ _DESCRIPTION = """\ DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology. It consists of 82191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of table schema, annotated with sentence description that covers all facts in the triple set. """ _URLs = { "train": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-train.json", "validation": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-dev.json", "test": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-test.json", } class Dart(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") DEFAULT_CONFIG_NAME = "dart" def _info(self): features = datasets.Features( { "gem_id": datasets.Value("string"), "gem_parent_id": datasets.Value("string"), "dart_id": datasets.Value("int32"), "tripleset": [[datasets.Value("string")]], # list of triples "subtree_was_extended": datasets.Value("bool"), "target_sources": [datasets.Value("string")], "target": datasets.Value("string"), # single target for train "references": [datasets.Value("string")], } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=datasets.info.SupervisedKeysData( input="tripleset", output="target" ), homepage="", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_dir = dl_manager.download_and_extract(_URLs) return [ datasets.SplitGenerator( name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl} ) for spl in ["train", "validation", "test"] ] def _generate_examples(self, filepath, split, filepaths=None, lang=None): """Yields examples.""" with open(filepath, encoding="utf-8") as f: data = json.loads(f.read()) id_ = -1 i = -1 for example in data: if split == "train": i += 1 for annotation in example["annotations"]: id_ += 1 yield id_, { "gem_id": f"dart-{split}-{id_}", "gem_parent_id": f"dart-{split}-{id_}", "dart_id": i, "tripleset": example["tripleset"], "subtree_was_extended": example.get( "subtree_was_extended", None ), # some are missing "target_sources": [ annotation["source"] for annotation in example["annotations"] ], "target": annotation["text"], "references": [], } else: id_ += 1 yield id_, { "gem_id": f"dart-{split}-{id_}", "gem_parent_id": f"dart-{split}-{id_}", "dart_id": id_, "tripleset": example["tripleset"], "subtree_was_extended": example.get( "subtree_was_extended", None ), # some are missing "target_sources": [ annotation["source"] for annotation in example["annotations"] ], "target": example["annotations"][0]["text"] if len(example["annotations"]) > 0 else "", "references": [ annotation["text"] for annotation in example["annotations"] ], }