AmazonScience/massive

1# coding=utf-82 3"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""4 5import json6import datasets7 8logger = datasets.logging.get_logger(__name__)9 10_DESCRIPTION = """\11        MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations12        for the Natural Language Understanding tasks of intent prediction and slot annotation.13        Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing14        the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.15"""16 17_CITATION = """\18        @misc{fitzgerald2022massive,19              title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},20              author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},21              year={2022},22              eprint={2204.08582},23              archivePrefix={arXiv},24              primaryClass={cs.CL}25        }26        \27        @inproceedings{bastianelli-etal-2020-slurp,28            title = "{SLURP}: A Spoken Language Understanding Resource Package",29            author = "Bastianelli, Emanuele  and30              Vanzo, Andrea  and31              Swietojanski, Pawel  and32              Rieser, Verena",33            booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",34            month = nov,35            year = "2020",36            address = "Online",37            publisher = "Association for Computational Linguistics",38            url = "https://aclanthology.org/2020.emnlp-main.588",39            doi = "10.18653/v1/2020.emnlp-main.588",40            pages = "7252--7262",41            abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."42        }43"""44 45_LICENSE = """\46Copyright Amazon.com Inc. or its affiliates.47 48Attribution 4.0 International49 50=======================================================================51 52Creative Commons Corporation ("Creative Commons") is not a law firm and53does not provide legal services or legal advice. Distribution of54Creative Commons public licenses does not create a lawyer-client or55other relationship. 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For439the avoidance of doubt, this paragraph does not form part of the public440licenses.441 442Creative Commons may be contacted at creativecommons.org.443"""444# version 1.0445_URL0 = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz"446# version 1.1447_URL1 = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.1.tar.gz"448 449# version 1.0450_LANGUAGES0 = ['af-ZA', 'am-ET', 'ar-SA', 'az-AZ', 'bn-BD', 'cy-GB', 'da-DK', 'de-DE', 'el-GR', 'en-US',451             'es-ES', 'fa-IR', 'fi-FI', 'fr-FR', 'he-IL', 'hi-IN', 'hu-HU', 'hy-AM', 'id-ID', 'is-IS',452             'it-IT', 'ja-JP', 'jv-ID', 'ka-GE', 'km-KH', 'kn-IN', 'ko-KR', 'lv-LV', 'ml-IN', 'mn-MN',453             'ms-MY', 'my-MM', 'nb-NO', 'nl-NL', 'pl-PL', 'pt-PT', 'ro-RO', 'ru-RU', 'sl-SL', 'sq-AL',454             'sv-SE', 'sw-KE', 'ta-IN', 'te-IN', 'th-TH', 'tl-PH', 'tr-TR', 'ur-PK', 'vi-VN', 'zh-CN',455             'zh-TW']456 457# version 1.1 ('ca-ES' added)458_LANGUAGES1 = sorted(_LANGUAGES0 + ['ca-ES'])459 460_SCENARIOS = ['social', 'transport', 'calendar', 'play', 'news', 'datetime', 'recommendation', 'email',461              'iot', 'general', 'audio', 'lists', 'qa', 'cooking', 'takeaway', 'music', 'alarm', 'weather']462 463_INTENTS = ['datetime_query', 'iot_hue_lightchange', 'transport_ticket', 'takeaway_query', 'qa_stock',464            'general_greet', 'recommendation_events', 'music_dislikeness', 'iot_wemo_off', 'cooking_recipe',465            'qa_currency', 'transport_traffic', 'general_quirky', 'weather_query', 'audio_volume_up',466            'email_addcontact', 'takeaway_order', 'email_querycontact', 'iot_hue_lightup',467            'recommendation_locations', 'play_audiobook', 'lists_createoradd', 'news_query',468            'alarm_query', 'iot_wemo_on', 'general_joke', 'qa_definition', 'social_query',469            'music_settings', 'audio_volume_other', 'calendar_remove', 'iot_hue_lightdim',470            'calendar_query', 'email_sendemail', 'iot_cleaning', 'audio_volume_down',471            'play_radio', 'cooking_query', 'datetime_convert', 'qa_maths', 'iot_hue_lightoff',472            'iot_hue_lighton', 'transport_query', 'music_likeness', 'email_query', 'play_music',473            'audio_volume_mute', 'social_post', 'alarm_set', 'qa_factoid', 'calendar_set',474            'play_game', 'alarm_remove', 'lists_remove', 'transport_taxi', 'recommendation_movies',475            'iot_coffee', 'music_query', 'play_podcasts', 'lists_query']476 477_ALL = "all"478 479 480class MassiveConfig(datasets.BuilderConfig):481    """BuilderConfig for MASSIVE."""482 483    def __init__(self, dataset_version=None, *args, **kwargs):484        """BuilderConfig for MASSIVE.485        Args:486          **kwargs: keyword arguments forwarded to super.487        """488        super(MassiveConfig, self).__init__(*args, **kwargs)489        self.dataset_version = dataset_version if dataset_version else "1.0"490        self.data_url = _URL0 if self.dataset_version == "1.0" else _URL1491 492 493class MASSIVE(datasets.GeneratorBasedBuilder):494    """MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""495 496    # All individual locale datasets are served from the latest version.497    BUILDER_CONFIGS = [498        MassiveConfig(499            name = name,500            dataset_version = '1.1',501            version = datasets.Version("1.0.0"),502            description = f"The MASSIVE v1.1 corpora for {name}",503        ) for name in _LANGUAGES1504        ]505    # Version 1.0506    BUILDER_CONFIGS.append(MassiveConfig(507            name = _ALL,508            dataset_version = '1.0',509            version = datasets.Version("1.0.0"),510            description = f"The MASSIVE v1.0 corpora for entire corpus",511        )512    )513    # Version 1.1514    BUILDER_CONFIGS.append(MassiveConfig(515            name = _ALL + '_1.1', # Append '_1.1' to 'all' in order to get the latest version.516            dataset_version = '1.1',517            version = datasets.Version("1.0.0"),518            description = f"The MASSIVE v1.1 corpora for entire corpus",519        )520    )521 522    DEFAULT_CONFIG_NAME = _ALL523 524    def _info(self):525        return datasets.DatasetInfo(526            description=_DESCRIPTION,527            features=datasets.Features(528                {529                    "id": datasets.Value("string"),530                    "locale": datasets.Value("string"),531                    "partition": datasets.Value("string"),532                    "scenario": datasets.features.ClassLabel(names=_SCENARIOS),533                    "intent": datasets.features.ClassLabel(names=_INTENTS),534                    "utt": datasets.Value("string"),535                    "annot_utt": datasets.Value("string"),536                    "worker_id": datasets.Value("string"),537                    "slot_method": datasets.Sequence({538                        "slot": datasets.Value("string"),539                        "method": datasets.Value("string"),540                    }),541                    "judgments": datasets.Sequence({542                        "worker_id": datasets.Value("string"),543                        "intent_score": datasets.Value("int8"),544                        "slots_score": datasets.Value("int8"),545                        "grammar_score": datasets.Value("int8"),546                        "spelling_score": datasets.Value("int8"),547                        "language_identification": datasets.Value("string"),548                    }),549                },550            ),551            supervised_keys=None,552            homepage="https://github.com/alexa/massive",553            citation=_CITATION,554            license=_LICENSE,555        )556 557    def _split_generators(self, dl_manager):558 559        archive = dl_manager.download(self.config.data_url)560        return [561            datasets.SplitGenerator(562                name=datasets.Split.TRAIN,563                gen_kwargs={564                    "files": dl_manager.iter_archive(archive),565                    "split": "train",566                    "lang": self.config.name,567                }568            ),569            datasets.SplitGenerator(570                name=datasets.Split.VALIDATION,571                gen_kwargs={572                    "files": dl_manager.iter_archive(archive),573                    "split": "dev",574                    "lang": self.config.name,575                }576            ),577            datasets.SplitGenerator(578                name=datasets.Split.TEST,579                gen_kwargs={580                    "files": dl_manager.iter_archive(archive),581                    "split": "test",582                    "lang": self.config.name,583                }584            ),585        ]586 587    def _generate_examples(self, files, split, lang):588 589        key_ = 0590 591        if lang == "all_1.1":592            lang = _LANGUAGES1.copy()593        elif lang == "all":594            lang = _LANGUAGES0.copy()595        else:596            lang = [lang]597 598        logger.info("⏳ Generating examples from = %s", ", ".join(lang))599 600        for path, f in files:601 602            l = path.split("/")[-1].split(".")[0]603 604            if not lang:605                break606            elif l in lang:607                lang.remove(l)608            else:609                continue610 611            # Read the file612            lines = f.read().decode(encoding="utf-8").split("\n")613 614            for line in lines:615 616                data = json.loads(line)617 618                if data["partition"] != split:619                    continue620 621                # Slot method622                if "slot_method" in data:623                    slot_method = [624                        {625                            "slot": s["slot"],626                            "method": s["method"],627                        } for s in data["slot_method"]628                    ]629                else:630                    slot_method = []631 632                # Judgments633                if "judgments" in data:634                    judgments = [635                        {636                            "worker_id": j["worker_id"],637                            "intent_score": j["intent_score"],638                            "slots_score": j["slots_score"],639                            "grammar_score": j["grammar_score"],640                            "spelling_score": j["spelling_score"],641                            "language_identification": j["language_identification"] if "language_identification" in j else "target",642                        } for j in data["judgments"]643                    ]644                else:645                    judgments = []646 647                yield key_, {648                    "id": data["id"],649                    "locale": data["locale"],650                    "partition": data["partition"],651                    "scenario": data["scenario"],652                    "intent": data["intent"],653                    "utt": data["utt"],654                    "annot_utt": data["annot_utt"],655                    "worker_id": data["worker_id"],656                    "slot_method": slot_method,657                    "judgments": judgments,658                }659 660                key_ += 1661