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"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages""" |
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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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MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations |
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for the Natural Language Understanding tasks of intent prediction and slot annotation. |
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Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing |
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the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. |
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""" |
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_URL = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz" |
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_LANGUAGES = [ |
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"af-ZA", |
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"am-ET", |
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"ar-SA", |
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"az-AZ", |
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"bn-BD", |
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"cy-GB", |
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"da-DK", |
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"de-DE", |
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"el-GR", |
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"en-US", |
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"es-ES", |
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"fa-IR", |
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"fi-FI", |
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"fr-FR", |
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"he-IL", |
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"hi-IN", |
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"hu-HU", |
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"hy-AM", |
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"id-ID", |
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"is-IS", |
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"it-IT", |
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"ja-JP", |
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"jv-ID", |
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"ka-GE", |
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"km-KH", |
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"kn-IN", |
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"ko-KR", |
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"lv-LV", |
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"ml-IN", |
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"mn-MN", |
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"ms-MY", |
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"my-MM", |
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"nb-NO", |
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"nl-NL", |
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"pl-PL", |
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"pt-PT", |
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"ro-RO", |
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"ru-RU", |
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"sl-SL", |
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"sq-AL", |
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"sv-SE", |
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"sw-KE", |
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"ta-IN", |
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"te-IN", |
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"th-TH", |
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"tl-PH", |
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"tr-TR", |
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"ur-PK", |
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"vi-VN", |
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"zh-CN", |
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"zh-TW", |
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] |
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_SCENARIOS = [ |
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"social", |
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"transport", |
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"calendar", |
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"play", |
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"news", |
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"datetime", |
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"recommendation", |
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"email", |
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"iot", |
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"general", |
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"audio", |
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"lists", |
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"qa", |
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"cooking", |
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"takeaway", |
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"music", |
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"alarm", |
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"weather", |
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] |
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_INTENTS = [ |
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"datetime_query", |
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"iot_hue_lightchange", |
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"transport_ticket", |
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"takeaway_query", |
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"qa_stock", |
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"general_greet", |
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"recommendation_events", |
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"music_dislikeness", |
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"iot_wemo_off", |
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"cooking_recipe", |
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"qa_currency", |
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"transport_traffic", |
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"general_quirky", |
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"weather_query", |
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"audio_volume_up", |
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"email_addcontact", |
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"takeaway_order", |
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"email_querycontact", |
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"iot_hue_lightup", |
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"recommendation_locations", |
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"play_audiobook", |
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"lists_createoradd", |
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"news_query", |
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"alarm_query", |
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"iot_wemo_on", |
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"general_joke", |
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"qa_definition", |
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"social_query", |
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"music_settings", |
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"audio_volume_other", |
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"calendar_remove", |
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"iot_hue_lightdim", |
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"calendar_query", |
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"email_sendemail", |
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"iot_cleaning", |
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"audio_volume_down", |
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"play_radio", |
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"cooking_query", |
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"datetime_convert", |
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"qa_maths", |
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"iot_hue_lightoff", |
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"iot_hue_lighton", |
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"transport_query", |
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"music_likeness", |
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"email_query", |
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"play_music", |
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"audio_volume_mute", |
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"social_post", |
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"alarm_set", |
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"qa_factoid", |
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"calendar_set", |
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"play_game", |
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"alarm_remove", |
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"lists_remove", |
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"transport_taxi", |
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"recommendation_movies", |
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"iot_coffee", |
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"music_query", |
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"play_podcasts", |
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"lists_query", |
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] |
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class MASSIVE(datasets.GeneratorBasedBuilder): |
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"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages""" |
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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"The MASSIVE corpora for {name}", |
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) |
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for name in _LANGUAGES |
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] |
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DEFAULT_CONFIG_NAME = "en-US" |
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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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"locale": datasets.Value("string"), |
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"partition": datasets.Value("string"), |
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"scenario": datasets.features.ClassLabel(names=_SCENARIOS), |
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"intent": datasets.features.ClassLabel(names=_INTENTS), |
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"utt": datasets.Value("string"), |
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"annot_utt": datasets.Value("string"), |
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"worker_id": datasets.Value("string"), |
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"slot_method": datasets.Sequence( |
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{ |
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"slot": datasets.Value("string"), |
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"method": datasets.Value("string"), |
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} |
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), |
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"judgments": datasets.Sequence( |
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{ |
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"worker_id": datasets.Value("string"), |
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"intent_score": datasets.Value("int8"), |
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"slots_score": datasets.Value("int8"), |
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"grammar_score": datasets.Value("int8"), |
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"spelling_score": datasets.Value("int8"), |
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"language_identification": datasets.Value("string"), |
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} |
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), |
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}, |
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), |
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supervised_keys=None, |
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homepage="https://github.com/alexa/massive", |
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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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archive_path = dl_manager.download(_URL) |
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files = dl_manager.iter_archive(archive_path) |
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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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"files": files, |
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"split": "train", |
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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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"files": files, |
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"split": "dev", |
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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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"files": files, |
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"split": "test", |
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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, files, split, lang): |
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filepath = "1.0/data/" + lang + ".jsonl" |
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logger.info("⏳ Generating examples from = %s", filepath) |
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for path, f in files: |
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if path == filepath: |
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lines = f.readlines() |
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key_ = 0 |
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for line in lines: |
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print("line:", line) |
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data = json.loads(line) |
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if data["partition"] != split: |
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continue |
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if "slot_method" in data: |
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slot_method = [ |
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{ |
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"slot": s["slot"], |
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"method": s["method"], |
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} |
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for s in data["slot_method"] |
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] |
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else: |
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slot_method = [] |
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if "judgments" in data: |
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judgments = [ |
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{ |
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"worker_id": j["worker_id"], |
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"intent_score": j["intent_score"], |
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"slots_score": j["slots_score"], |
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"grammar_score": j["grammar_score"], |
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"spelling_score": j["spelling_score"], |
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"language_identification": j["language_identification"], |
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} |
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for j in data["judgments"] |
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] |
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else: |
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judgments = [] |
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yield key_, { |
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"id": data["id"], |
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"locale": data["locale"], |
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"partition": data["partition"], |
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"scenario": data["scenario"], |
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"intent": data["intent"], |
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"utt": data["utt"], |
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"annot_utt": data["annot_utt"], |
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"worker_id": data["worker_id"], |
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"slot_method": slot_method, |
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"judgments": judgments, |
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} |
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key_ += 1 |
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