Upload CoQAR.py

CoQAR.py

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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. 
+
+![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