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# 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.
import json
import datasets
_CITATION = """\
@inproceedings{feng-etal-2022-emowoz,
title = "{E}mo{WOZ}: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems",
author = "Feng, Shutong and
Lubis, Nurul and
Geishauser, Christian and
Lin, Hsien-chin and
Heck, Michael and
van Niekerk, Carel and
Gasic, Milica",
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-1.436",
pages = "4096--4113",
abstract = "The ability to recognise emotions lends a conversational artificial intelligence a human \
touch. While emotions in chit-chat dialogues have received substantial attention, emotions in \
task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success \
having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora \
are limited in size, label richness, and public availability, creating a bottleneck for downstream \
tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a \
large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, \
a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K \
emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect \
human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions \
that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this \
is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, \
which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability \
of this corpus for emotion recognition and state tracking in task-oriented dialogues.",
}
"""
_DESCRIPTION = """\
EmoWOZ is a user emotion recognition in task-oriented dialogues dataset, \
consisting all dialogues from MultiWOZ and 1000 additional human-machine \
dialogues (DialMAGE). Each user utterance is annotated with one of the \
following emotions: 0: neutral, 1: fearful, 2: dissatisfied, 3: apologetic, \
4: abusive, 5: excited, 6: satisfied. System utterances are annotated with \
-1. For detailed label design and explanation, please refer to the paper and \
dataset homepage.
"""
_HOMEPAGE = "https://zenodo.org/record/6506504"
_LICENSE = "https://creativecommons.org/licenses/by-nc/4.0/legalcode"
_URLS = {
"emowoz_multiwoz": "https://zenodo.org/record/6506504/files/emowoz-multiwoz.json",
"emowoz_dialmage": "https://zenodo.org/record/6506504/files/emowoz-dialmage.json",
"emowoz_split": "https://zenodo.org/record/6506504/files/data-split.json"
}
class EmoWOZ(datasets.GeneratorBasedBuilder):
"""EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="emowoz", version=VERSION, description="This part contains all user-emotion-annotated dialogues from EmoWOZ"),
datasets.BuilderConfig(name="multiwoz", version=VERSION, description="This part contains all user-emotion-annotated dialogues from MultiWOZ"),
datasets.BuilderConfig(name="dialmage", version=VERSION, description="This part contains all user-emotion-annotated dialogues from human-machine interactions (DialMAGE)"),
]
DEFAULT_CONFIG_NAME = "emowoz"
def _info(self):
features = datasets.Features(
{
"dialogue_id": datasets.Value("string"),
"log": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"emotion": datasets.ClassLabel(names=[-1,0,1,2,3,4,5,6])
}
)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
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={
"multiwoz_filepath": data_dir['emowoz_multiwoz'],
"dialmage_filepath": data_dir['emowoz_dialmage'],
"split_filepath": data_dir['emowoz_split'],
"split": "train"
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"multiwoz_filepath": data_dir['emowoz_multiwoz'],
"dialmage_filepath": data_dir['emowoz_dialmage'],
"split_filepath": data_dir['emowoz_split'],
"split": "dev"
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"multiwoz_filepath": data_dir['emowoz_multiwoz'],
"dialmage_filepath": data_dir['emowoz_dialmage'],
"split_filepath": data_dir['emowoz_split'],
"split": "test"
},
)
]
def _generate_examples(self, multiwoz_filepath, dialmage_filepath, split_filepath, split):
with open(multiwoz_filepath, encoding="utf-8") as f:
multiwoz_dialogues = json.load(f)
with open(dialmage_filepath, encoding="utf-8") as f:
dialmage_dialogues = json.load(f)
dialogues = {**multiwoz_dialogues, **dialmage_dialogues}
with open(split_filepath, encoding="utf-8") as f:
data_split = json.load(f)
if self.config.name == 'emowoz':
dial_ids = data_split[split]['multiwoz'] + data_split[split]['dialmage']
else:
dial_ids = data_split[split][self.config.name]
# resolve the one duplicate key in the training set of emowoz/data-split.json
dial_ids = list(set(dial_ids))
for key in dial_ids:
yield key, {
"dialogue_id": key,
"log": {
"text": [log['text'] for log in dialogues[key]['log']],
"emotion": [a['emotion'][3]["emotion"] if i%2 == 0 else -1 for i, a in enumerate(dialogues[key]['log'])]
}
}
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