emowoz.py

# 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'],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # 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'],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # 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]

        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'])]
                }
            }