Datasets:

AmazonScience
/

mintaka

	# coding=utf-8

	"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""

	import json
	import datasets

	logger = datasets.logging.get_logger(__name__)

	_DESCRIPTION = """\
	Mintaka is a complex, natural, and multilingual dataset designed for experimenting with end-to-end
	question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English,
	annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian,
	Japanese, Portuguese, and Spanish for a total of 180,000 samples.
	Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions,
	which were naturally elicited from crowd workers.
	"""

	_CITATION = """\
	@inproceedings{sen-etal-2022-mintaka,
	title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
	author = "Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir",
	booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
	month = oct,
	year = "2022",
	address = "Gyeongju, Republic of Korea",
	publisher = "International Committee on Computational Linguistics",
	url = "https://aclanthology.org/2022.coling-1.138",
	pages = "1604--1619"
	}
	"""

	_LICENSE = """\
	Copyright Amazon.com Inc. or its affiliates.
	Attribution 4.0 International
	"""

	_TRAIN_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_train.json"
	_DEV_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_dev.json"
	_TEST_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_test.json"


	_LANGUAGES = ['en', 'ar', 'de', 'ja', 'hi', 'pt', 'es', 'it', 'fr']

	_ALL = "all"

	class Mintaka(datasets.GeneratorBasedBuilder):
	"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name = name,
	version = datasets.Version("1.0.0"),
	description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering for {name}",
	) for name in _LANGUAGES
	]

	BUILDER_CONFIGS.append(datasets.BuilderConfig(
	name = _ALL,
	version = datasets.Version("1.0.0"),
	description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
	))

	DEFAULT_CONFIG_NAME = 'en'

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"lang": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answerText": datasets.Value("string"),
	"category": datasets.Value("string"),
	"complexityType": datasets.Value("string"),
	"questionEntity": [{
	"name": datasets.Value("string"),
	"entityType": datasets.Value("string"),
	"label": datasets.Value("string"),
	"mention": datasets.Value("string"),
	"span": [datasets.Value("int32")],
	}],
	"answerEntity": [{
	"name": datasets.Value("string"),
	"label": datasets.Value("string"),
	}]
	},
	),
	supervised_keys=None,
	citation=_CITATION,
	license=_LICENSE,
	)

	def _split_generators(self, dl_manager):
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"file": dl_manager.download_and_extract(_TRAIN_URL),
	"lang": self.config.name,
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"file": dl_manager.download_and_extract(_DEV_URL),
	"lang": self.config.name,
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"file": dl_manager.download_and_extract(_TEST_URL),
	"lang": self.config.name,
	}
	),
	]

	def _generate_examples(self, file, lang):
	if lang == _ALL:
	langs = _LANGUAGES
	else:
	langs = [lang]

	key_ = 0

	logger.info("⏳ Generating examples from = %s", ", ".join(lang))

	with open(file, encoding='utf-8') as json_file:
	data = json.load(json_file)
	for lang in langs:
	for sample in data:
	questionEntity = [
	{
	"name": str(qe["name"]),
	"entityType": qe["entityType"],
	"label": qe["label"] if "label" in qe else "",
	# span only applies for English question
	"mention": qe["mention"] if lang == "en" else None,
	"span": qe["span"] if lang == "en" else [],
	} for qe in sample["questionEntity"]
	]

	answers = []
	if sample['answer']["answerType"] == "entity" and sample['answer']['answer'] is not None:
	answers = sample['answer']['answer']
	elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]:
	answers = sample['answer']['supportingEnt']

	# helper to get language for the corresponding language
	def get_label(labels, lang):
	if lang in labels:
	return labels[lang]
	if 'en' in labels:
	return labels['en']
	return None

	answerEntity = [
	{
	"name": str(ae["name"]),
	"label": get_label(ae["label"], lang),
	} for ae in answers
	]

	yield key_, {
	"id": sample["id"],
	"lang": lang,
	"question": sample["question"] if lang == 'en' else sample['translations'][lang],
	"answerText": sample["answer"]["mention"],
	"category": sample["category"],
	"complexityType": sample["complexityType"],
	"questionEntity": questionEntity,
	"answerEntity": answerEntity,
	}

	key_ += 1