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# 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.
"""
CoQAR is a corpus containing 4.5K conversations from the open-source dataset [Conversational Question-Answering dataset CoQA](https://stanfordnlp.github.io/coqa/), for a total of 53K follow-up question-answer pairs.
In CoQAR each original question was manually annotated with at least 2 at most 3 out-of-context rewritings.
COQAR can be used for (at least) three NLP tasks: question paraphrasing, question rewriting and conversational question answering.
We annotated each original question of CoQA with at least 2 at most 3 out-of-context rewritings.
The annotations are published under the licence CC-BY-SA 4.0.
The original content of the dataset CoQA is under the distinct licences described below.
The corpus CoQA contains passages from seven domains, which are public under the following licenses:
- Literature and Wikipedia passages are shared under CC BY-SA 4.0 license.
- Children's stories are collected from MCTest which comes with MSR-LA license.
- Middle/High school exam passages are collected from RACE which comes with its own license.
- News passages are collected from the DeepMind CNN dataset which comes with Apache license (see [K. M. Hermann, T. Kočiský and E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, P. Blunsom, Teaching Machines to Read and Comprehend. Advances in Neural Information Processing Systems (NIPS), 2015](http://arxiv.org/abs/1506.03340)).
"""
import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{brabant-etal-2022-coqar,
title = "{C}o{QAR}: Question Rewriting on {C}o{QA}",
author = "Brabant, Quentin and
Lecorv{\'e}, Gw{\'e}nol{\'e} and
Rojas Barahona, Lina M.",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.13",
pages = "119--126"
}
"""
_DESCRIPTION = """\
CoQAR is a corpus containing 4.5K conversations from the open-source dataset [Conversational Question-Answering dataset CoQA](https://stanfordnlp.github.io/coqa/), for a total of 53K follow-up question-answer pairs.
In CoQAR each original question was manually annotated with at least 2 at most 3 out-of-context rewritings.
COQAR can be used for (at least) three NLP tasks: question paraphrasing, question rewriting and conversational question answering.
We annotated each original question of CoQA with at least 2 at most 3 out-of-context rewritings.
The annotations are published under the licence CC-BY-SA 4.0.
The original content of the dataset CoQA is under the distinct licences described below.
The corpus CoQA contains passages from seven domains, which are public under the following licenses:
- Literature and Wikipedia passages are shared under CC BY-SA 4.0 license.
- Children's stories are collected from MCTest which comes with MSR-LA license.
- Middle/High school exam passages are collected from RACE which comes with its own license.
- News passages are collected from the DeepMind CNN dataset which comes with Apache license (see [K. M. Hermann, T. Kočiský and E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, P. Blunsom, Teaching Machines to Read and Comprehend. Advances in Neural Information Processing Systems (NIPS), 2015](http://arxiv.org/abs/1506.03340)).
"""
_HOMEPAGE = "https://github.com/Orange-OpenSource/COQAR/"
_LICENSE = """
- Annotations, litterature and Wikipedia passages: licence CC-BY-SA 4.0.
- Children's stories are from MCTest (MSR-LA license).
- Exam passages come from RACE which has its own license.
- News passages are from the DeepMind CNN dataset (Apache license).
"""
_URLS = {
"train": "https://raw.githubusercontent.com/Orange-OpenSource/COQAR/master/data/CoQAR/train/coqar-train-v1.0.json",
"dev": "https://raw.githubusercontent.com/Orange-OpenSource/COQAR/master/data/CoQAR/dev/coqar-dev-v1.0.json"
}
class CoQAR(datasets.GeneratorBasedBuilder):
"""
CoQAR is a corpus containing 4.5K conversations from the open-source dataset [Conversational Question-Answering dataset CoQA](https://stanfordnlp.github.io/coqa/), for a total of 53K follow-up question-answer pairs.
In CoQAR each original question was manually annotated with at least 2 at most 3 out-of-context rewritings.
COQAR can be used for (at least) three NLP tasks: question paraphrasing, question rewriting and conversational question answering.
"""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
'conversation_id' : datasets.Value("string"),
'turn_id': datasets.Value("int16"),
'original_question' : datasets.Value("string"),
'question_paraphrases' : datasets.Sequence(feature=datasets.Value("string")),
'answer' : datasets.Value("string"),
'answer_span_start' : datasets.Value("int32"),
'answer_span_end' : datasets.Value("int32"),
'answer_span_text' : datasets.Value("string"),
'conversation_history' : datasets.Sequence(feature=datasets.Value("string")),
'file_name' : datasets.Value("string"),
'story': datasets.Value("string"),
'name': datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features,
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir['train'],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir['dev'],
"split": "dev",
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
with open(filepath, 'r') as f:
dic = json.load(f)
i = 0
for datum in dic['data']:
history = []
for question, answer in zip(datum['questions'], datum['answers']):
yield i, {
'conversation_id' : datum['id'],
'turn_id': question['turn_id'],
'original_question' :question['input_text'],
'question_paraphrases' : question['paraphrase'],
'answer' : answer['input_text'],
'answer_span_start' : answer['span_start'],
'answer_span_end' : answer['span_end'],
'answer_span_text' : answer['span_text'],
'conversation_history' : list(history),
'file_name' : datum['filename'],
'story': datum['story'],
'name': datum['name']
}
history.append(question['input_text'])
history.append(answer['input_text'])
i+=1 |