| |
|
|
| """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 |
|
|
