# 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. ''' _URLS = { 'test': 'DiaBLa.json', } class DiablaConfig(datasets.BuilderConfig): '''BuilderConfig for DiaBLa.''' def __init__(self, **kwargs): """BuilderConfig for DiaBLa. Args: **kwargs: keyword arguments forwarded to super. """ super(DiablaConfig, self).__init__(**kwargs) class Diabla(datasets.GeneratorBasedBuilder): '''DiaBLa: English-French parallel dataset of bilingual dialogue''' BUILDER_CONFIGS = [ DiablaConfig( name='plain_text', version=datasets.Version('1.0.0', ''), description='Plain text', ), ] #TODO def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { 'id': datasets.Value('string'), 'orig': datasets.Value('string'), 'norm': datasets.Value('string'), 'mt': datasets.Value('string'), 'ref': datasets.Value('string'), 'utterance_meta': { 'eval-judgment': datasets.Value("string"), 'eval-verbatim': datasets.Value('string'), 'eval-problems': [ datasets.Value("string") ], 'lang': datasets.Value("string") }, 'dialogue_meta': { 'start_time': datasets.Value('string'), 'end_time' : datasets.Value('string'), 'translation_model': datasets.Value('string'), 'final_evaluation_user1': { 'style': datasets.Value("string"), 'coherence': datasets.Value("string"), 'grammaticality': datasets.Value("string"), 'meaning': datasets.Value("string"), 'word_choice': datasets.Value("string"), }, 'final_evaluation_user2': { 'style': datasets.Value("string"), 'coherence': datasets.Value("string"), 'grammaticality': datasets.Value("string"), 'meaning': datasets.Value("string"), 'word_choice': datasets.Value("string"), }, 'scenario': [[ datasets.Value("string") ]], 'user1': { 'role_num': datasets.Value('int64'), 'role':[ datasets.Value('string') ], 'initiated_dialogue': datasets.Value('bool'), 'turn_number': datasets.Value('int64'), 'lang': datasets.Value("string"), }, 'user2':{ 'role_num': datasets.Value('int64'), 'role':[ datasets.Value('string') ], 'initiated_dialogue': datasets.Value('bool'), 'turn_number': datasets.Value('int64'), 'lang': datasets.Value("string"), } }, 'dialogue_history': [ { 'id': datasets.Value('string'), 'orig': datasets.Value('string'), 'norm': datasets.Value('string'), 'mt': datasets.Value('string'), 'ref': datasets.Value('string'), 'utterance_meta': { 'eval-judgment': datasets.Value("string"), 'eval-verbatim': datasets.Value("string"), 'eval-problems': [ datasets.Value("string") ], 'lang': datasets.Value("string"), } } ] } ), # TODO? 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['test']})] 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'] for user in 'user1', 'user2': dialogue[user]['role_num'] = dialogue[user].get('role_num', dialogue[user]['rolenum']) for info_to_remove in ['eval-stage', 'useragent', 'rolenum']: if info_to_remove in dialogue[user]: del dialogue[user][info_to_remove] dialogue_info = {k: dialogue[k] for k in dialogue_info_keys} if dialogue_info['end_time'] is None: dialogue_info['end_time'] = '' for info_to_remove in ['interface','verbatim_quality', 'particular_problems', 'tech', 'would_use', 'timestamp', 'technical_issue']: for final_eval in 'final_evaluation_user1', 'final_evaluation_user2': if info_to_remove in dialogue_info[final_eval]: del dialogue_info[final_eval][info_to_remove] # Main data: the utterances for utterance_id in dialogue['utterances']: utterance = dialogue['utterances'][utterance_id] # Meta-information attached to the utterance utterance_info_keys = ['judgment', 'verbatim', 'problems'] utterance_info = {'eval-' + k: utterance['eval'][k] for k in utterance_info_keys} if utterance_info['eval-judgment'] is None: utterance_info['eval-judgment'] = '' utterance_info['lang'] = 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': original_text, 'norm': normalised_text, 'mt': mt_text, 'id': id_, 'ref': reference_text, 'utterance_meta': utterance_info } # add to history (without dialogue info and history) dialogue_history.append(utterance_instance.copy()) utterance_instance['dialogue_meta'] = dialogue_info utterance_instance['dialogue_history'] = dialogue_history yield id_, utterance_instance