Datasets:
disfl_qa

	# coding=utf-8
	# 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.
	"""A Benchmark Dataset for Understanding Disfluencies in Question Answering"""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	_CITATION = """\
	@inproceedings{gupta-etal-2021-disflqa,
	title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
	author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
	booktitle = "Findings of ACL",
	year = "2021"
	}

	"""

	_DESCRIPTION = """\
	Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
	namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
	dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
	a source of distractors.

	The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90% of the disfluencies are
	corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a
	major gap between speech and NLP research community. We hope the dataset can serve as a benchmark dataset for
	testing robustness of models against disfluent inputs.

	Our expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from
	Disfl-QA. Detailed experiments and analyses can be found in our paper.
	"""

	_HOMEPAGE = "https://github.com/google-research-datasets/disfl-qa"

	_LICENSE = "Disfl-QA dataset is licensed under CC BY 4.0"

	_URL = "https://raw.githubusercontent.com/google-research-datasets/Disfl-QA/main/"

	_URLS_squad_v2 = {
	"train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/" + "train-v2.0.json",
	"dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/" + "dev-v2.0.json",
	}


	class DisflQA(datasets.GeneratorBasedBuilder):
	"""A Benchmark Dataset for Understanding Disfluencies in Question Answering"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"squad_v2_id": datasets.Value("string"),
	"original question": datasets.Value("string"),
	"disfluent question": datasets.Value("string"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	),
	}
	)
	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, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="disfluent question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	squad_v2_downloaded_files = dl_manager.download_and_extract(_URLS_squad_v2)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": dl_manager.download_and_extract(_URL + "train.json"),
	"split": "train",
	"squad_v2_data": squad_v2_downloaded_files,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": dl_manager.download_and_extract(_URL + "test.json"),
	"split": "test",
	"squad_v2_data": squad_v2_downloaded_files,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": dl_manager.download_and_extract(_URL + "dev.json"),
	"split": "dev",
	"squad_v2_data": squad_v2_downloaded_files,
	},
	),
	]

	def _generate_examples(
	self,
	filepath,
	split,
	squad_v2_data, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	"""Yields examples as (key, example) tuples."""

	merge_squad_v2_json = {}

	for file in squad_v2_data:
	with open(squad_v2_data[file], encoding="utf-8") as f:
	merge_squad_v2_json.update(json.load(f))

	squad_v2_dict = _helper_dict(merge_squad_v2_json) # contains all squad_v2 data in a dict with id as key

	with open(filepath, encoding="utf-8") as f:
	glob_id = 0
	for id_, row in enumerate(f):
	data = json.loads(row)
	for i in data:
	yield glob_id, {
	"squad_v2_id": i,
	"disfluent question": data[i]["disfluent"],
	"title": squad_v2_dict[i]["title"],
	"context": squad_v2_dict[i]["context"],
	"original question": squad_v2_dict[i]["question"],
	"answers": {
	"answer_start": squad_v2_dict[i]["answers"]["answer_start"],
	"text": squad_v2_dict[i]["answers"]["text"],
	},
	}
	glob_id += 1


	def _helper_dict(row_squad_v2: dict): # creates dict with id as key for combined squad_v2

	squad_v2_dict = {}

	for example in row_squad_v2["data"]:
	title = example.get("title", "").strip()
	for paragraph in example["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	id_ = qa["id"]

	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"].strip() for answer in qa["answers"]]

	squad_v2_dict[id_] = {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}
	return squad_v2_dict