Datasets:

GEM
/

dart

	import json
	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"]
	],
	}