# 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