Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
File size: 3,544 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""WebQuestions Benchmark for Question Answering."""
import json
import re
import datasets
_CITATION = """
@inproceedings{berant-etal-2013-semantic,
title = "Semantic Parsing on {F}reebase from Question-Answer Pairs",
author = "Berant, Jonathan and
Chou, Andrew and
Frostig, Roy and
Liang, Percy",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1160",
pages = "1533--1544",
}
"""
_SPLIT_DOWNLOAD_URL = {
"train": "https://worksheets.codalab.org/rest/bundles/0x4a763f8cde224c2da592b75f29e2f5c2/contents/blob/",
"test": "https://worksheets.codalab.org/rest/bundles/0xe7bac352fce7448c9ef238fb0a297ec2/contents/blob/",
}
_DESCRIPTION = """\
This dataset consists of 6,642 question/answer pairs.
The questions are supposed to be answerable by Freebase, a large knowledge graph.
The questions are mostly centered around a single named entity.
The questions are popular ones asked on the web (at least in 2013).
"""
class WebQuestions(datasets.GeneratorBasedBuilder):
"""WebQuestions Benchmark for Question Answering."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"url": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
homepage="https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
file_paths = dl_manager.download(_SPLIT_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=split, gen_kwargs={"file_path": file_path})
for split, file_path in file_paths.items()
]
def _generate_examples(self, file_path):
"""Parses split file and yields examples."""
def _target_to_answers(target):
target = re.sub(r"^\(list |\)$", "", target)
return ["".join(ans) for ans in re.findall(r'\(description (?:"([^"]+?)"|([^)]+?))\)\w*', target)]
with open(file_path, encoding="utf-8") as f:
examples = json.load(f)
for i, ex in enumerate(examples):
yield i, {
"url": ex["url"],
"question": ex["utterance"],
"answers": _target_to_answers(ex["targetValue"]),
}
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