Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
expert-generated
Annotations Creators:
no-annotation
Source Datasets:
original
License:
"""A dataset reducing relation extraction to simple reading comprehension questions""" | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{levy-etal-2017-zero, | |
title = "Zero-Shot Relation Extraction via Reading Comprehension", | |
author = "Levy, Omer and | |
Seo, Minjoon and | |
Choi, Eunsol and | |
Zettlemoyer, Luke", | |
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)", | |
month = aug, | |
year = "2017", | |
address = "Vancouver, Canada", | |
publisher = "Association for Computational Linguistics", | |
url = "https://www.aclweb.org/anthology/K17-1034", | |
doi = "10.18653/v1/K17-1034", | |
pages = "333--342", | |
} | |
""" | |
_DESCRIPTION = """\ | |
A dataset reducing relation extraction to simple reading comprehension questions | |
""" | |
_DATA_URL = "http://nlp.cs.washington.edu/zeroshot/relation_splits.tar.bz2" | |
class QaZre(datasets.GeneratorBasedBuilder): | |
"""QA-ZRE: Reducing relation extraction to simple reading comprehension questions""" | |
VERSION = datasets.Version("0.1.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"relation": datasets.Value("string"), | |
"question": datasets.Value("string"), | |
"subject": datasets.Value("string"), | |
"context": datasets.Value("string"), | |
"answers": datasets.features.Sequence(datasets.Value("string")), | |
} | |
), | |
# 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="http://nlp.cs.washington.edu/zeroshot", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
dl_dir = dl_manager.download_and_extract(_DATA_URL) | |
dl_dir = os.path.join(dl_dir, "relation_splits") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepaths": [os.path.join(dl_dir, "test." + str(i)) for i in range(10)], | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepaths": [os.path.join(dl_dir, "dev." + str(i)) for i in range(10)], | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepaths": [os.path.join(dl_dir, "train." + str(i)) for i in range(10)], | |
}, | |
), | |
] | |
def _generate_examples(self, filepaths): | |
"""Yields examples.""" | |
for file_idx, filepath in enumerate(filepaths): | |
with open(filepath, encoding="utf-8") as f: | |
data = csv.reader(f, delimiter="\t") | |
for idx, row in enumerate(data): | |
yield f"{file_idx}_{idx}", { | |
"relation": row[0], | |
"question": row[1], | |
"subject": row[2], | |
"context": row[3], | |
"answers": row[4:], | |
} | |