Upload DiaBLa.py

DiaBLa.py

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+# coding=utf-8
+'''DiaBLA: Dialogue Bilingue datset'''
+
+import json
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = '''\
+@article{bawden_DiaBLa:-A-Corpus-of_2021,
+  author = {Bawden, Rachel and Bilinski, Eric and Lavergne, Thomas and Rosset, Sophie},
+  doi = {10.1007/s10579-020-09514-4},
+  title = {DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation},
+  year = {2021},
+  journal = {Language Resources and Evaluation},
+  publisher = {Springer Verlag},
+  volume = {55},
+  pages = {635--660},
+  url = {https://hal.inria.fr/hal-03021633},
+  pdf = {https://hal.inria.fr/hal-03021633/file/diabla-lre-personal-formatting.pdf},
+}
+'''
+
+_DESCRIPTION = '''\
+English-French parallel dataset for the evaluation of \
+Machine Translation (MT) for informal, written bilingual dialogue.
+'''
+ 
+#_URL = 'https://github.com/rbawden/DiaBLa-dataset'
+#_URLS = {
+#    'dialogues': _URL + '/DiaBLa-corpus/all-dialogues.json',
+#    'users': _URL + '/DiaBLa-corpus/all-users.json'
+#}
+
+
+class DiablaConfig(datasets.BuilderConfig):
+    '''BuilderConfig for DiaBLa.'''
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUAD.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadConfig, self).__init__(**kwargs)
+
+
+class Diabla(datasets.GeneratorBasedBuilder):
+    '''DiaBLa: English-French parallel dataset of bilingual dialogue'''
+
+    BUILDER_CONFIGS = [
+        SquadConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage='https://github.com/rbawden/DiaBLa-dataset'
+            citation=_CITATION,
+            task_templates=[
+                # TODO
+            ],
+        )
+
+    #def _split_generators(self, dl_manager):
+    #    downloaded_files = dl_manager.download_and_extract(_URLS)
+
+    #    return [
+    #        datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+    #        datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+    #    ]
+
+    def _generate_examples(self, filepath):
+        '''This function returns the examples in the raw (text) form.'''
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            diabla = json.load(f)
+            for dialogue_name in sorted(diabla['dialogues']):
+                dialogue_history = [] # to store past utterances
+                dialogue = diabla['dialogues'][dialogue_name]
+                # Meta-information attached to the dialogue
+                dialogue_info_keys = ['start_time', 'end_time', 'scenario',
+                                      'user1', 'user2', 'translation_model',
+                                      'final_evaluation_user1', 'final_evaluation_user2']
+                dialogue_info = {k: dialogue[k] for k in dialogue_info_keys}
+                # Main data: the utterances
+                for utterance_id in dialogue['utterances']:
+                    utterance = utterances[utterance_id]
+                    # Meta-information attached to the utterance
+                    utterance_info_keys = ['judgment', 'verbatim', 'problems', 'user']
+                    utterance_info = {'eval-' + k: utterance['eval'][k] for k in utterance_info_keys}
+                    utterance_info['language'] = utterance['language']
+                    # Utterance text
+                    original_text = utterance['original_text']
+                    mt_text = utterance['postprocessed_text']
+                    reference_text = utterance['reference_translation']
+                    normalised_text = utterance['normalised_version']
+                    id_ = dialogue_name + '_' + utterance_id
+                    utterance_instance = {
+                        'orig_text': original_text,
+                        'norm_text': normalised_text,
+                        'mt_text': mt_text,
+                        'id': id_,
+                        'ref_text': reference_text,
+                        'utterance_meta_info': utterance_info
+                        'context': dialogue_history
+                    }
+                    # add to history (without dialogue info)
+                    dialogue_history.append(utterance_instance.copy()) 
+                    utterance_instance['dialogue_meta_info'] = utterance_info
+                    yield id_, utterance_instance