Datasets:

dwadden
/

healthver_entailment

	"""Scientific fact-checking dataset. Verifies claims based on citation sentences
	using evidence from the cited abstracts. Formatted as a paragraph-level entailment task."""


	import datasets
	import json


	_CITATION = """\
	@inproceedings{Sarrouti2021EvidencebasedFO,
	title={Evidence-based Fact-Checking of Health-related Claims},
	author={Mourad Sarrouti and Asma Ben Abacha and Yassine Mrabet and Dina Demner-Fushman},
	booktitle={Conference on Empirical Methods in Natural Language Processing},
	year={2021},
	url={https://api.semanticscholar.org/CorpusID:244119074}
	}
	"""


	_DESCRIPTION = """\
	HealthVer is a dataset of public health claims, verified against scientific research articles. For this version of the dataset, we follow the preprocessing from the MultiVerS modeling paper https://github.com/dwadden/multivers, verifying claims against full article abstracts rather than individual sentences. Entailment labels and rationales are included.
	"""

	_URL = "https://scifact.s3.us-west-2.amazonaws.com/longchecker/latest/data.tar.gz"


	def flatten(xss):
	return [x for xs in xss for x in xs]


	class HealthVerEntailmentConfig(datasets.BuilderConfig):
	"""builderconfig for healthver"""

	def __init__(self, **kwargs):
	"""

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(HealthVerEntailmentConfig, self).__init__(
	version=datasets.Version("1.0.0", ""), **kwargs
	)


	class HealthVerEntailment(datasets.GeneratorBasedBuilder):
	"""TODO(healthver): Short description of my dataset."""

	# TODO(healthver): Set up version.
	VERSION = datasets.Version("0.1.0")

	def _info(self):
	# TODO(healthver): Specifies the datasets.DatasetInfo object

	features = {
	"claim_id": datasets.Value("int32"),
	"claim": datasets.Value("string"),
	"abstract_id": datasets.Value("int32"),
	"title": datasets.Value("string"),
	"abstract": datasets.features.Sequence(datasets.Value("string")),
	"verdict": datasets.Value("string"),
	"evidence": datasets.features.Sequence(datasets.Value("int32")),
	}

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	features
	# These are the features of your dataset like images, labels ...
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	citation=_CITATION,
	)

	@staticmethod
	def _read_tar_file(f):
	res = []
	for row in f:
	this_row = json.loads(row.decode("utf-8"))
	res.append(this_row)

	return res

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(healthver): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	archive = dl_manager.download(_URL)
	for path, f in dl_manager.iter_archive(archive):
	# The claims are too similar to paper titles; don't include.
	if path == "data/healthver/corpus.jsonl":
	corpus = self._read_tar_file(f)
	corpus = {x["doc_id"]: x for x in corpus}
	elif path == "data/healthver/claims_train.jsonl":
	claims_train = self._read_tar_file(f)
	elif path == "data/healthver/claims_dev.jsonl":
	claims_validation = self._read_tar_file(f)
	elif path == "data/healthver/claims_test.jsonl":
	claims_test = self._read_tar_file(f)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"claims": claims_train,
	"corpus": corpus,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"claims": claims_validation,
	"corpus": corpus,
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"claims": claims_test,
	"corpus": corpus,
	"split": "test",
	},
	),
	]

	def _generate_examples(self, claims, corpus, split):
	"""Yields examples."""
	# Loop over claims and put evidence together with claim.
	id_ = -1 # Will increment to 0 on first iteration.
	for claim in claims:
	evidence = {int(k): v for k, v in claim["evidence"].items()}
	for cited_doc_id in claim["doc_ids"]:
	cited_doc = corpus[cited_doc_id]
	abstract_sents = [sent.strip() for sent in cited_doc["abstract"]]

	if cited_doc_id in evidence:
	this_evidence = evidence[cited_doc_id]
	verdict = this_evidence[0][
	"label"
	] # Can take first evidence since all labels are same.
	evidence_sents = flatten(
	[entry["sentences"] for entry in this_evidence]
	)
	else:
	verdict = "NEI"
	evidence_sents = []

	instance = {
	"claim_id": claim["id"],
	"claim": claim["claim"],
	"abstract_id": cited_doc_id,
	"title": cited_doc["title"],
	"abstract": abstract_sents,
	"verdict": verdict,
	"evidence": evidence_sents,
	}

	id_ += 1
	yield id_, instance