Update emowoz.py

Cleaning up comments.

emowoz.py

@@ -11,19 +11,13 @@
 # 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",
@@ -41,12 +35,23 @@ _CITATION = """\
     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.",
+    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 \
@@ -57,15 +62,10 @@ following emotions: 0: neutral, 1: fearful, 2: dissatisfied, 3: apologetic, \
 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",
@@ -73,34 +73,22 @@ _URLS = {
 }
 
 
-
 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="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 = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "emowoz"
 
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-            
+    
+    def _info(self):     
+        
         features = datasets.Features(
             {
                 "dialogue_id": datasets.Value("string"),
@@ -121,19 +109,14 @@ class EmoWOZ(datasets.GeneratorBasedBuilder):
             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'],
@@ -143,7 +126,6 @@ class EmoWOZ(datasets.GeneratorBasedBuilder):
             ),
             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'],
@@ -153,7 +135,6 @@ class EmoWOZ(datasets.GeneratorBasedBuilder):
             ),
             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'],
@@ -163,10 +144,9 @@ class EmoWOZ(datasets.GeneratorBasedBuilder):
             )
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+
     def _generate_examples(self, multiwoz_filepath, dialmage_filepath, split_filepath, split):
-        # 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: