| | |
| |
|
| | """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 "", |
| | |
| | "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'] |
| |
|
| | |
| | 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 |
| |
|
