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"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering""" |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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Mintaka is a complex, natural, and multilingual dataset designed for experimenting with end-to-end |
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question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, |
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annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, |
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Japanese, Portuguese, and Spanish for a total of 180,000 samples. |
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Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, |
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which were naturally elicited from crowd workers. |
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""" |
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_CITATION = """\ |
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@inproceedings{sen-etal-2022-mintaka, |
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title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering", |
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author = "Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir", |
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
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month = oct, |
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year = "2022", |
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address = "Gyeongju, Republic of Korea", |
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publisher = "International Committee on Computational Linguistics", |
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url = "https://aclanthology.org/2022.coling-1.138", |
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pages = "1604--1619" |
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} |
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""" |
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_LICENSE = """\ |
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Copyright Amazon.com Inc. or its affiliates. |
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Attribution 4.0 International |
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""" |
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_TRAIN_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_train.json" |
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_DEV_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_dev.json" |
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_TEST_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_test.json" |
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_LANGUAGES = ['en', 'ar', 'de', 'ja', 'hi', 'pt', 'es', 'it', 'fr'] |
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_ALL = "all" |
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class Mintaka(datasets.GeneratorBasedBuilder): |
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"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name = name, |
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version = datasets.Version("1.0.0"), |
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description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering for {name}", |
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) for name in _LANGUAGES |
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] |
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BUILDER_CONFIGS.append(datasets.BuilderConfig( |
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name = _ALL, |
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version = datasets.Version("1.0.0"), |
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description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering", |
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)) |
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DEFAULT_CONFIG_NAME = 'en' |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"lang": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answerText": datasets.Value("string"), |
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"category": datasets.Value("string"), |
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"complexityType": datasets.Value("string"), |
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"questionEntity": [{ |
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"name": datasets.Value("string"), |
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"entityType": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"mention": datasets.Value("string"), |
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"span": [datasets.Value("int32")], |
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}], |
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"answerEntity": [{ |
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"name": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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}] |
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}, |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"file": dl_manager.download_and_extract(_TRAIN_URL), |
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"lang": self.config.name, |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"file": dl_manager.download_and_extract(_DEV_URL), |
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"lang": self.config.name, |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"file": dl_manager.download_and_extract(_TEST_URL), |
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"lang": self.config.name, |
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} |
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), |
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] |
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def _generate_examples(self, file, lang): |
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if lang == _ALL: |
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langs = _LANGUAGES |
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else: |
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langs = [lang] |
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key_ = 0 |
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logger.info("⏳ Generating examples from = %s", ", ".join(lang)) |
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with open(file, encoding='utf-8') as json_file: |
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data = json.load(json_file) |
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for lang in langs: |
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for sample in data: |
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questionEntity = [ |
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{ |
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"name": str(qe["name"]), |
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"entityType": qe["entityType"], |
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"label": qe["label"] if "label" in qe else "", |
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"mention": qe["mention"] if lang == "en" else None, |
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"span": qe["span"] if lang == "en" else [], |
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} for qe in sample["questionEntity"] |
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] |
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answers = [] |
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if sample['answer']["answerType"] == "entity" and sample['answer']['answer'] is not None: |
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answers = sample['answer']['answer'] |
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elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]: |
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answers = sample['answer']['supportingEnt'] |
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def get_label(labels, lang): |
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if lang in labels: |
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return labels[lang] |
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if 'en' in labels: |
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return labels['en'] |
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return None |
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answerEntity = [ |
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{ |
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"name": str(ae["name"]), |
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"label": get_label(ae["label"], lang), |
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} for ae in answers |
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] |
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yield key_, { |
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"id": sample["id"], |
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"lang": lang, |
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"question": sample["question"] if lang == 'en' else sample['translations'][lang], |
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"answerText": sample["answer"]["mention"], |
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"category": sample["category"], |
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"complexityType": sample["complexityType"], |
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"questionEntity": questionEntity, |
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"answerEntity": answerEntity, |
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} |
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key_ += 1 |
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