Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
knowledge-base-qa
License:
File size: 6,222 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
"""GrailQA: The Strongly Generalizable Question Answering Dataset"""
from __future__ import absolute_import, division, print_function
import json
import logging
import os
import datasets
_CITATION = """\
@misc{gu2020iid,
title={Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases},
author={Yu Gu and Sue Kase and Michelle Vanni and Brian Sadler and Percy Liang and Xifeng Yan and Yu Su},
year={2020},
eprint={2011.07743},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
Strongly Generalizable Question Answering (GrailQA) is a new large-scale, \
high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated \
with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). \
It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot.
"""
_URL = "https://dl.orangedox.com/WyaCpL?dl=1"
class GrailQA(datasets.GeneratorBasedBuilder):
"""GrailQA: The Strongly Generalizable Question Answering Dataset"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"qid": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.features.Sequence(
{
"answer_type": datasets.Value("string"),
"answer_argument": datasets.Value("string"),
"entity_name": datasets.Value("string"),
}
),
"function": datasets.Value("string"),
"num_node": datasets.Value("int32"),
"num_edge": datasets.Value("int32"),
"graph_query": {
"nodes": datasets.features.Sequence(
{
"nid": datasets.Value("int32"),
"node_type": datasets.Value("string"),
"id": datasets.Value("string"),
"class": datasets.Value("string"),
"friendly_name": datasets.Value("string"),
"question_node": datasets.Value("int32"),
"function": datasets.Value("string"),
}
),
"edges": datasets.features.Sequence(
{
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"relation": datasets.Value("string"),
"friendly_name": datasets.Value("string"),
}
),
},
"sparql_query": datasets.Value("string"),
"domains": datasets.features.Sequence(datasets.Value("string")),
"level": datasets.Value("string"),
"s_expression": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://dki-lab.github.io/GrailQA/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_path = os.path.join(dl_manager.download_and_extract(_URL), "GrailQA_v1.0")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(dl_path, "grailqa_v1.0_train.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(dl_path, "grailqa_v1.0_dev.json")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(dl_path, "grailqa_v1.0_test_public.json")},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logging.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
samples = json.load(f)
for sample in samples:
features = {
"qid": str(sample["qid"]),
"question": sample["question"],
"function": sample.get("function", ""),
"num_node": sample.get("num_node", -1),
"num_edge": sample.get("num_edge", -1),
"graph_query": sample.get("graph_query", {"nodes": [], "edges": []}),
"sparql_query": sample.get("sparql_query", ""),
"domains": sample.get("domains", []),
"level": sample.get("level", ""),
"s_expression": sample.get("s_expression", ""),
}
answers = sample.get("answer", [])
for answer in answers:
if "entity_name" not in answer:
answer["entity_name"] = ""
features["answer"] = answers
yield sample["qid"], features
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