Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
Chinese
Size:
10K - 100K
ArXiv:
License:
File size: 6,701 Bytes
f6c86fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""C3 Parallel Corpora"""
from __future__ import absolute_import, division, print_function
import json
import datasets
_CITATION = """\
@article{sun2019investigating,
title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/1904.09679v3}
}
"""
_DESCRIPTION = """\
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.
"""
_URL = "https://raw.githubusercontent.com/nlpdata/c3/master/data/"
class C3Config(datasets.BuilderConfig):
""" BuilderConfig for NewDataset"""
def __init__(self, type_, **kwargs):
"""
Args:
pair: the language pair to consider
zip_file: The location of zip file containing original data
**kwargs: keyword arguments forwarded to super.
"""
self.type_ = type_
super().__init__(**kwargs)
class C3(datasets.GeneratorBasedBuilder):
"""C3 is the first free-form multiple-Choice Chinese machine reading Comprehension dataset, containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second language examinations."""
VERSION = datasets.Version("1.0.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
BUILDER_CONFIG_CLASS = C3Config
BUILDER_CONFIGS = [
C3Config(
name="mixed",
description="Mixed genre questions",
version=datasets.Version("1.0.0"),
type_="mixed",
),
C3Config(
name="dialog",
description="Dialog questions",
version=datasets.Version("1.0.0"),
type_="dialog",
),
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"documents": datasets.Sequence(datasets.Value("string")),
"document_id": datasets.Value("string"),
"questions": datasets.Sequence(
{
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"choice": datasets.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="https://github.com/nlpdata/c3",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# m or d
T = self.config.type_[0]
files = [_URL + f"c3-{T}-{split}.json" for split in ["train", "test", "dev"]]
dl_dir = dl_manager.download_and_extract(files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filename": dl_dir[0],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filename": dl_dir[1],
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filename": dl_dir[2],
"split": "dev",
},
),
]
def _generate_examples(self, filename, split):
""" Yields examples. """
with open(filename, "r", encoding="utf-8") as sf:
data = json.load(sf)
for id_, (documents, questions, document_id) in enumerate(data):
yield id_, {
"documents": documents,
"questions": questions,
"document_id": document_id,
}
|