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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 Dataset loading script for the QA-Discourse dataset (Pyatkin et. al., ACL 2020)."""
import datasets
from pathlib import Path
from typing import List
import pandas as pd
_CITATION = """\
@inproceedings{pyatkin2020qadiscourse,
title={QADiscourse-Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines},
author={Pyatkin, Valentina and Klein, Ayal and Tsarfaty, Reut and Dagan, Ido},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
pages={2804--2819},
year={2020}
}"""
_DESCRIPTION = """\
The dataset contains question-answer pairs to model discourse relations.
While answers roughly correspond to spans of the sentence, these spans could have been freely adjusted by annotators to grammaticaly fit the question;
Therefore, answers are given just as text and not as identified spans of the original sentence.
See the paper for details: QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines, Pyatkin et. al., 2020
"""
_HOMEPAGE = "https://github.com/ValentinaPy/QADiscourse"
_LICENSE = """Resources on this page are licensed CC-BY 4.0, a Creative Commons license requiring Attribution (https://creativecommons.org/licenses/by/4.0/)."""
_URLs = {
"wikinews.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_train.tsv",
"wikinews.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_dev.tsv",
"wikinews.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_test.tsv",
"wikipedia.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_train.tsv",
"wikipedia.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_dev.tsv",
"wikipedia.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_test.tsv",
}
COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer',
'question_start', 'question_aux', 'question_body', 'answer',
'untokenized sentence', 'target indices for untok sent']
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class QaDiscourse(datasets.GeneratorBasedBuilder):
"""QA-Discourse: Discourse Relations as Question-Answer Pairs. """
VERSION = datasets.Version("1.0.2")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text", version=VERSION, description="This provides the QA-Discourse dataset"
),
]
DEFAULT_CONFIG_NAME = (
"plain_text" # It's not mandatory to have a default configuration. Just use one if it make sense.
)
def _info(self):
features = datasets.Features(
{
"sentence": datasets.Value("string"),
"sent_id": datasets.Value("string"),
"question": datasets.Sequence(datasets.Value("string")),
"answers": datasets.Sequence(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, # 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,
)
def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
"""Returns SplitGenerators."""
# Download and prepare all files - keep same structure as _URLs
corpora = {section: Path(dl_manager.download_and_extract(_URLs[section]))
for section in _URLs}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepaths": [corpora["wikinews.train"],
corpora["wikipedia.train"]],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepaths": [corpora["wikinews.dev"],
corpora["wikipedia.dev"]],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepaths": [corpora["wikinews.test"],
corpora["wikipedia.test"]],
},
),
]
def _generate_examples(self, filepaths: List[str]):
"""
Yields QA-Discourse examples from a tsv file.
Sentences with no QAs will yield an ``empty QA'' record, where both 'question' and 'answers' are empty lists.
"""
# merge annotations from sections
df = pd.concat([pd.read_csv(fn, sep='\t', error_bad_lines=False) for fn in filepaths]).reset_index(drop=True)
df = df.applymap(str) # must turn all values to strings explicitly to avoid type errors
for counter, row in df.iterrows():
# Prepare question (3 "slots" and question mark)
question = [row.question_start, row.question_aux, row.question_body.rstrip('?'), '?']
answer = [row.answer]
if row.question_start == "_": # sentence has no QAs
question = []
answer = []
yield counter, {
"sentence": row.sentence,
"sent_id": row.qasrl_id,
"question": question,
"answers": answer,
}
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