Datasets:

rbawden
/

DiaBLa

	# coding=utf-8
	'''DiaBLA: "Dialogue Bilingue" Bilingual dialogue dataset'''

	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"),
	}
	}
	]
	}
	),
	supervised_keys=None,
	homepage='https://github.com/rbawden/DiaBLa-dataset',
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [datasets.SplitGenerator(name=datasets.Split.TEST, 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].get('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 final_eval in 'final_evaluation_user1', 'final_evaluation_user2':
	# Initialise when empty
	if dialogue_info[final_eval] == {}:
	dialogue_info[final_eval] = {'grammaticality': '', 'meaning': '',
	'coherence': '', 'style': '', 'word_choice': ''}
	# Remove some information
	for info_to_remove in ['interface','verbatim_quality',
	'particular_problems', 'tech',
	'would_use', 'timestamp', 'technical_issue']:
	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.replace('’', "'").replace('…', '...'), # normalise apostrophes to be the same as mt
	'utterance_meta': utterance_info
	}

	# add to history (without dialogue info and history)
	minimal_utterance = utterance_instance.copy()
	utterance_instance['dialogue_meta'] = dialogue_info
	utterance_instance['dialogue_history'] = dialogue_history.copy()
	dialogue_history.append(minimal_utterance)
	yield id_, utterance_instance