Datasets:
Tasks:
Text Classification
Sub-tasks:
fact-checking
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
File size: 7,915 Bytes
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"""Scientific fact-checking dataset. Verifies claims based on citation sentences
using evidence from the cited abstracts."""
import json
import datasets
_CITATION = """\
@inproceedings{Wadden2020FactOF,
title={Fact or Fiction: Verifying Scientific Claims},
author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
booktitle={EMNLP},
year={2020},
}
"""
_DESCRIPTION = """\
SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
"""
_URL = "https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"
class ScifactConfig(datasets.BuilderConfig):
"""BuilderConfig for Scifact"""
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ScifactConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Scifact(datasets.GeneratorBasedBuilder):
"""TODO(scifact): Short description of my dataset."""
# TODO(scifact): Set up version.
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
ScifactConfig(name="corpus", description=" The corpus of evidence documents"),
ScifactConfig(name="claims", description=" The claims are split into train, test, dev"),
]
def _info(self):
# TODO(scifact): Specifies the datasets.DatasetInfo object
if self.config.name == "corpus":
features = {
"doc_id": datasets.Value("int32"), # The document's S2ORC ID.
"title": datasets.Value("string"), # The title.
"abstract": datasets.features.Sequence(
datasets.Value("string")
), # The abstract, written as a list of sentences.
"structured": datasets.Value("bool"), # Indicator for whether this is a structured abstract.
}
else:
features = {
"id": datasets.Value("int32"), # An integer claim ID.
"claim": datasets.Value("string"), # The text of the claim.
"evidence_doc_id": datasets.Value("string"),
"evidence_label": datasets.Value("string"), # Label for the rationale.
"evidence_sentences": datasets.features.Sequence(datasets.Value("int32")), # Rationale sentences.
"cited_doc_ids": datasets.features.Sequence(datasets.Value("int32")), # The claim's "cited documents".
}
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
homepage="https://scifact.apps.allenai.org/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(scifact): 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)
if self.config.name == "corpus":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "data/corpus.jsonl",
"split": "train",
"files": dl_manager.iter_archive(archive),
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "data/claims_train.jsonl",
"split": "train",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "data/claims_test.jsonl",
"split": "test",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "data/claims_dev.jsonl",
"split": "dev",
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, filepath, split, files):
"""Yields examples."""
# TODO(scifact): Yields (key, example) tuples from the dataset
for path, f in files:
if path == filepath:
for id_, row in enumerate(f):
data = json.loads(row.decode("utf-8"))
if self.config.name == "corpus":
yield id_, {
"doc_id": int(data["doc_id"]),
"title": data["title"],
"abstract": data["abstract"],
"structured": data["structured"],
}
else:
if split == "test":
yield id_, {
"id": data["id"],
"claim": data["claim"],
"evidence_doc_id": "",
"evidence_label": "",
"evidence_sentences": [],
"cited_doc_ids": [],
}
else:
evidences = data["evidence"]
if evidences:
for id1, doc_id in enumerate(evidences):
for id2, evidence in enumerate(evidences[doc_id]):
yield str(id_) + "_" + str(id1) + "_" + str(id2), {
"id": data["id"],
"claim": data["claim"],
"evidence_doc_id": doc_id,
"evidence_label": evidence["label"],
"evidence_sentences": evidence["sentences"],
"cited_doc_ids": data.get("cited_doc_ids", []),
}
else:
yield id_, {
"id": data["id"],
"claim": data["claim"],
"evidence_doc_id": "",
"evidence_label": "",
"evidence_sentences": [],
"cited_doc_ids": data.get("cited_doc_ids", []),
}
break
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