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# 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 sample in data:
for lang in langs:
questionEntity = [
{
"name": str(qe["name"]),
"entityType": qe["entityType"],
"label": qe["label"] if "label" in qe else "",
"mention": qe["mention"],
"span": qe["span"],
} 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']
def get_label(labels, lang):
if lang in labels:
return labels[lang]
if 'en' in labels:
return labels['en']
return ""
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
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