Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
License:
File size: 4,428 Bytes
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# coding=utf-8
# Copyright 2020 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
"""DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension"""
from __future__ import absolute_import, division, print_function
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{sundream2018,
title={{DREAM}: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension},
author={Sun, Kai and Yu, Dian and Chen, Jianshu and Yu, Dong and Choi, Yejin and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2019},
url={https://arxiv.org/abs/1902.00164v1}
}
"""
_DESCRIPTION = """\
DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. \
In contrast to existing reading comprehension datasets, DREAM is the first to focus on \
in-depth multi-turn multi-party dialogue understanding.
"""
_URL = "https://raw.githubusercontent.com/nlpdata/dream/master/data/"
_URLS = {
"train": _URL + "train.json",
"dev": _URL + "dev.json",
"test": _URL + "test.json",
}
class DreamConfig(datasets.BuilderConfig):
"""BuilderConfig for Dream."""
def __init__(self, **kwargs):
"""BuilderConfig for Dream.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(DreamConfig, self).__init__(**kwargs)
class Dream(datasets.GeneratorBasedBuilder):
"""DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
DreamConfig(
name="plain_text",
version=datasets.Version("1.0.0"),
description="plain_text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"dialogue_id": datasets.Value("string"),
"dialogue": datasets.Sequence(datasets.Value("string")),
"question": datasets.Value("string"),
"choice": datasets.features.Sequence(datasets.Value("string")),
"answer": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://dataset.org/dream/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
dialogues = json.load(f)
counter = 0
for dialogue in dialogues:
dialogue_text = dialogue[0]
questions = dialogue[1]
dialogue_id = dialogue[2]
for que in questions:
yield counter, {
"id": counter,
"dialogue_id": dialogue_id,
"dialogue": dialogue_text,
"question": que["question"],
"choice": que["choice"],
"answer": que["answer"],
}
counter += 1
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