circa / circa.py
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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.
"""Dataset containing polar questions and indirect answers."""
from __future__ import absolute_import, division, print_function
import csv
import datasets
_CITATION = """\
@InProceedings{louis_emnlp2020,
author = "Annie Louis and Dan Roth and Filip Radlinski",
title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing",
year = "2020",
}
"""
_DESCRIPTION = """\
The Circa (meaning ‘approximately’) dataset aims to help machine learning systems
to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with
annotations for the interpretation of the answer. The data is collected in 10
different social conversational situations (eg. food preferences of a friend).
NOTE: There might be missing labels in the dataset and we have replaced them with -1.
The original dataset contains no train/dev/test splits.
"""
_LICENSE = "Creative Commons Attribution 4.0 License"
_DATA_URL = "https://raw.githubusercontent.com/google-research-datasets/circa/main/circa-data.tsv"
class Circa(datasets.GeneratorBasedBuilder):
"""Dataset containing polar questions and indirect answers."""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"context": datasets.Value("string"),
"question-X": datasets.Value("string"),
"canquestion-X": datasets.Value("string"),
"answer-Y": datasets.Value("string"),
"judgements": datasets.Value("string"),
"goldstandard1": datasets.features.ClassLabel(
names=[
"Yes",
"No",
"In the middle, neither yes nor no",
"Probably yes / sometimes yes",
"Probably no",
"Yes, subject to some conditions",
"Other",
"I am not sure how X will interpret Y’s answer",
]
),
"goldstandard2": datasets.features.ClassLabel(
names=[
"Yes",
"No",
"In the middle, neither yes nor no",
"Yes, subject to some conditions",
"Other",
]
),
}
)
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="https://github.com/google-research-datasets/circa",
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": train_path,
"split": datasets.Split.TRAIN,
},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
goldstandard1_labels = [
"Yes",
"No",
"In the middle, neither yes nor no",
"Probably yes / sometimes yes",
"Probably no",
"Yes, subject to some conditions",
"Other",
"I am not sure how X will interpret Y’s answer",
]
goldstandard2_labels = [
"Yes",
"No",
"In the middle, neither yes nor no",
"Yes, subject to some conditions",
"Other",
]
data = csv.reader(f, delimiter="\t")
next(data, None) # skip the headers
for id_, row in enumerate(data):
row = [x if x != "nan" else -1 for x in row]
_, context, question_X, canquestion_X, answer_Y, judgements, goldstandard1, goldstandard2 = row
if goldstandard1 not in goldstandard1_labels:
goldstandard1 = -1
if goldstandard2 not in goldstandard2_labels:
goldstandard2 = -1
yield id_, {
"context": context,
"question-X": question_X,
"canquestion-X": canquestion_X,
"answer-Y": answer_Y,
"judgements": judgements,
"goldstandard1": goldstandard1,
"goldstandard2": goldstandard2,
}