CoQAR.py

# 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. 

![image](https://user-images.githubusercontent.com/52821991/165952155-822ce743-791d-46c8-8705-0937a69df933.png)

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. 

![image](https://user-images.githubusercontent.com/52821991/165952155-822ce743-791d-46c8-8705-0937a69df933.png)

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