Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 8,042 Bytes
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# 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
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