Datasets:

hhu-dsml
/

emowoz

	# 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.
	# TODO: Address all TODOs and remove all explanatory comments
	"""TODO: Add a description here."""


	import csv
	import json
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_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.",
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_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.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://zenodo.org/record/6506504"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "https://creativecommons.org/licenses/by-nc/4.0/legalcode"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_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")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="train-all", version=VERSION, description="This part contains the training set of all user-emotion-annotated dialogues from EmoWOZ"),
	datasets.BuilderConfig(name="dev-all", version=VERSION, description="This part contains the development set of all user-emotion-annotated dialogues from EmoWOZ"),
	datasets.BuilderConfig(name="test-all", version=VERSION, description="This part contains the test set of all user-emotion-annotated dialogues from EmoWOZ"),
	datasets.BuilderConfig(name="train-multiwoz", version=VERSION, description="This part contains the training set of user-emotion-annotated dialogues from MultiWOZ"),
	datasets.BuilderConfig(name="dev-multiwoz", version=VERSION, description="This part contains the development set of user-emotion-annotated dialogues from MultiWOZ"),
	datasets.BuilderConfig(name="test-multiwoz", version=VERSION, description="This part contains the test set of user-emotion-annotated dialogues from MultiWOZ"),
	datasets.BuilderConfig(name="train-dialmage", version=VERSION, description="This part contains the training set of user-emotion-annotated dialogues from human-machine interaction (DialMAGE)"),
	datasets.BuilderConfig(name="dev-dialmage", version=VERSION, description="This part contains the development set of user-emotion-annotated dialogues from human-machine interaction (DialMAGE)"),
	datasets.BuilderConfig(name="test-dialmage", version=VERSION, description="This part contains the test set of user-emotion-annotated dialogues from human-machine interaction (DialMAGE)"),
	]

	# DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

	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):
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	data_dir = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"multiwoz_filepath": data_dir['emowoz_multiwoz'],
	"dialmage_filepath": data_dir['emowoz_dialmage'],
	"split_filepath": data_dir['emowoz_split'],
	},
	)
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, multiwoz_filepath, dialmage_filepath, split_filepath):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	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)

	split, subset = self.config.name.split('-')
	if subset == 'all':
	dial_ids = data_split[split]['multiwoz'] + data_split[split]['dialmage']
	else:
	dial_ids = data_split[split][subset]

	# resolve the 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'])]
	}
	}