import json from typing import List import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, 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}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", 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." } """ _DATASETNAME = "massive" _DESCRIPTION = """\ MASSIVE dataset—Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assistant utterances spanning 18 domains, 60 intents, and 55 slots. MASSIVE was created by tasking professional translators to localize the English-only SLURP dataset into 50 typologically diverse languages, including 8 native languages and 2 other languages mostly spoken in Southeast Asia. """ _HOMEPAGE = "https://github.com/alexa/massive" _LICENSE = Licenses.CC_BY_4_0.value _LOCAL = False _LANGUAGES = ["ind", "jav", "khm", "zlm", "mya", "tha", "tgl", "vie"] _URLS = { _DATASETNAME: "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.1.tar.gz", } _SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION, Tasks.SLOT_FILLING] _SOURCE_VERSION = "1.1.0" _SEACROWD_VERSION = "2024.06.20" # ind, jav, khm, zlm, mya, tha, tgl, vie, cmn, tam _LANGS = [ "af-ZA", "am-ET", "ar-SA", "az-AZ", "bn-BD", "cy-GB", "da-DK", "de-DE", "el-GR", "en-US", "es-ES", "fa-IR", "fi-FI", "fr-FR", "he-IL", "hi-IN", "hu-HU", "hy-AM", "id-ID", # ind "is-IS", "it-IT", "ja-JP", "jv-ID", # jav "ka-GE", "km-KH", # khm "kn-IN", "ko-KR", "lv-LV", "ml-IN", "mn-MN", "ms-MY", # zlm "my-MM", # mya "nb-NO", "nl-NL", "pl-PL", "pt-PT", "ro-RO", "ru-RU", "sl-SL", "sq-AL", "sv-SE", "sw-KE", "ta-IN", "te-IN", "th-TH", # tha "tl-PH", # tgl "tr-TR", "ur-PK", "vi-VN", # vie "zh-CN", # cmn "zh-TW", ] _SUBSETS = ["id-ID", "jv-ID", "km-KH", "ms-MY", "my-MM", "th-TH", "tl-PH", "vi-VN"] _SCENARIOS = ["calendar", "recommendation", "social", "general", "news", "cooking", "iot", "email", "weather", "alarm", "transport", "lists", "takeaway", "play", "audio", "music", "qa", "datetime"] _INTENTS = [ "audio_volume_other", "play_music", "iot_hue_lighton", "general_greet", "calendar_set", "audio_volume_down", "social_query", "audio_volume_mute", "iot_wemo_on", "iot_hue_lightup", "audio_volume_up", "iot_coffee", "takeaway_query", "qa_maths", "play_game", "cooking_query", "iot_hue_lightdim", "iot_wemo_off", "music_settings", "weather_query", "news_query", "alarm_remove", "social_post", "recommendation_events", "transport_taxi", "takeaway_order", "music_query", "calendar_query", "lists_query", "qa_currency", "recommendation_movies", "general_joke", "recommendation_locations", "email_querycontact", "lists_remove", "play_audiobook", "email_addcontact", "lists_createoradd", "play_radio", "qa_stock", "alarm_query", "email_sendemail", "general_quirky", "music_likeness", "cooking_recipe", "email_query", "datetime_query", "transport_traffic", "play_podcasts", "iot_hue_lightchange", "calendar_remove", "transport_query", "transport_ticket", "qa_factoid", "iot_cleaning", "alarm_set", "datetime_convert", "iot_hue_lightoff", "qa_definition", "music_dislikeness", ] _TAGS = [ "O", "B-food_type", "B-movie_type", "B-person", "B-change_amount", "I-relation", "I-game_name", "B-date", "B-movie_name", "I-person", "I-place_name", "I-podcast_descriptor", "I-audiobook_name", "B-email_folder", "B-coffee_type", "B-app_name", "I-time", "I-coffee_type", "B-transport_agency", "B-podcast_descriptor", "I-playlist_name", "B-media_type", "B-song_name", "I-music_descriptor", "I-song_name", "B-event_name", "I-timeofday", "B-alarm_type", "B-cooking_type", "I-business_name", "I-color_type", "B-podcast_name", "I-personal_info", "B-weather_descriptor", "I-list_name", "B-transport_descriptor", "I-game_type", "I-date", "B-place_name", "B-color_type", "B-game_name", "I-artist_name", "I-drink_type", "B-business_name", "B-timeofday", "B-sport_type", "I-player_setting", "I-transport_agency", "B-game_type", "B-player_setting", "I-music_album", "I-event_name", "I-general_frequency", "I-podcast_name", "I-cooking_type", "I-radio_name", "I-joke_type", "I-meal_type", "I-transport_type", "B-joke_type", "B-time", "B-order_type", "B-business_type", "B-general_frequency", "I-food_type", "I-time_zone", "B-currency_name", "B-time_zone", "B-ingredient", "B-house_place", "B-audiobook_name", "I-ingredient", "I-media_type", "I-news_topic", "B-music_genre", "I-definition_word", "B-list_name", "B-playlist_name", "B-email_address", "I-currency_name", "I-movie_name", "I-device_type", "I-weather_descriptor", "B-audiobook_author", "I-audiobook_author", "I-app_name", "I-order_type", "I-transport_name", "B-radio_name", "I-business_type", "B-definition_word", "B-artist_name", "I-movie_type", "B-transport_name", "I-email_folder", "B-music_album", "I-house_place", "I-music_genre", "B-drink_type", "I-alarm_type", "B-music_descriptor", "B-news_topic", "B-meal_type", "I-transport_descriptor", "I-email_address", "I-change_amount", "B-device_type", "B-transport_type", "B-relation", "I-sport_type", "B-personal_info", ] class MASSIVEDataset(datasets.GeneratorBasedBuilder): """MASSIVE datasets contains datasets to detect the intent from the text and fill the dialogue slots""" BUILDER_CONFIGS = ( [ SEACrowdConfig( name=f"massive_{subset}_source", version=datasets.Version(_SOURCE_VERSION), description=f"MASSIVE source schema for {subset}", schema="source", subset_id="massive_" + subset, ) for subset in _SUBSETS ] + [ SEACrowdConfig( name=f"massive_{subset}_seacrowd_text", version=datasets.Version(_SEACROWD_VERSION), description=f"MASSIVE Nusantara intent classification schema for {subset}", schema="seacrowd_text", subset_id="massive_intent_" + subset, ) for subset in _SUBSETS ] + [ SEACrowdConfig( name=f"massive_{subset}_seacrowd_seq_label", version=datasets.Version(_SEACROWD_VERSION), description=f"MASSIVE Nusantara slot filling schema for {subset}", schema="seacrowd_seq_label", subset_id="massive_slot_filling_" + subset, ) for subset in _SUBSETS ] + [ SEACrowdConfig( name="massive_source", version=datasets.Version(_SOURCE_VERSION), description="MASSIVE source schema", schema="source", subset_id="massive", ), SEACrowdConfig( name="massive_seacrowd_text", version=datasets.Version(_SEACROWD_VERSION), description="MASSIVE Nusantara intent classification schema", schema="seacrowd_text", subset_id="massive_intent", ), SEACrowdConfig( name="massive_seacrowd_seq_label", version=datasets.Version(_SEACROWD_VERSION), description="MASSIVE Nusantara slot filling schema", schema="seacrowd_seq_label", subset_id="massive_slot_filling", ), ] ) DEFAULT_CONFIG_NAME = "massive_id-ID_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "locale": datasets.Value("string"), "partition": datasets.Value("string"), "scenario": datasets.features.ClassLabel(names=_SCENARIOS), "intent": datasets.features.ClassLabel(names=_INTENTS), "utt": datasets.Value("string"), "annot_utt": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=_TAGS)), "worker_id": datasets.Value("string"), "slot_method": datasets.Sequence( { "slot": datasets.Value("string"), "method": datasets.Value("string"), } ), "judgments": datasets.Sequence( { "worker_id": datasets.Value("string"), "intent_score": datasets.Value("int8"), # [0, 1, 2] "slots_score": datasets.Value("int8"), # [0, 1, 2] "grammar_score": datasets.Value("int8"), # [0, 1, 2, 3, 4] "spelling_score": datasets.Value("int8"), # [0, 1, 2] "language_identification": datasets.Value("string"), } ), } ) elif self.config.schema == "seacrowd_text": features = schemas.text_features(label_names=_INTENTS) elif self.config.schema == "seacrowd_seq_label": features = schemas.seq_label_features(label_names=_TAGS) else: raise ValueError(f"Invalid config schema: {self.config.schema}") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: archive = dl_manager.download(_URLS[_DATASETNAME]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": dl_manager.iter_archive(archive), "split": "train", "lang": self.config.name, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "files": dl_manager.iter_archive(archive), "split": "dev", "lang": self.config.name, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "files": dl_manager.iter_archive(archive), "split": "test", "lang": self.config.name, }, ), ] def _get_bio_format(self, text): """This function is modified from https://huggingface.co/datasets/qanastek/MASSIVE/blob/main/MASSIVE.py""" tags, tokens = [], [] bio_mode = False cpt_bio = 0 current_tag = None split_iter = iter(text.split(" ")) for s in split_iter: if s.startswith("["): current_tag = s.strip("[") bio_mode = True cpt_bio += 1 next(split_iter) continue elif s.endswith("]"): bio_mode = False if cpt_bio == 1: prefix = "B-" else: prefix = "I-" token = prefix + current_tag word = s.strip("]") current_tag = None cpt_bio = 0 else: if bio_mode: if cpt_bio == 1: prefix = "B-" else: prefix = "I-" token = prefix + current_tag word = s cpt_bio += 1 else: token = "O" word = s tags.append(token) tokens.append(word) return tokens, tags def _generate_examples(self, files: list, split: str, lang: str): _id = 0 lang = lang.replace("massive_", "").replace("source", "").replace("seacrowd_text", "").replace("seacrowd_seq_label", "") if not lang: lang = _LANGS.copy() else: lang = [lang[:-1]] # logger.info("Generating examples from = %s", ", ".join(lang)) for path, f in files: curr_lang = path.split(f"{_SOURCE_VERSION[:-2]}/data/")[-1].split(".jsonl")[0] if not lang: break elif curr_lang in lang: lang.remove(curr_lang) else: continue # Read the file lines = f.read().decode(encoding="utf-8").split("\n") for line in lines: data = json.loads(line) if data["partition"] != split: continue # Slot method if "slot_method" in data: slot_method = [ { "slot": s["slot"], "method": s["method"], } for s in data["slot_method"] ] else: slot_method = [] # Judgments if "judgments" in data: judgments = [ { "worker_id": j["worker_id"], "intent_score": j["intent_score"], "slots_score": j["slots_score"], "grammar_score": j["grammar_score"], "spelling_score": j["spelling_score"], "language_identification": j["language_identification"] if "language_identification" in j else "target", } for j in data["judgments"] ] else: judgments = [] if self.config.schema == "source": tokens, tags = self._get_bio_format(data["annot_utt"]) yield _id, { "id": str(_id) + "_" + data["id"], "locale": data["locale"], "partition": data["partition"], "scenario": data["scenario"], "intent": data["intent"], "utt": data["utt"], "annot_utt": data["annot_utt"], "tokens": tokens, "ner_tags": tags, "worker_id": data["worker_id"], "slot_method": slot_method, "judgments": judgments, } elif self.config.schema == "seacrowd_seq_label": tokens, tags = self._get_bio_format(data["annot_utt"]) yield _id, { "id": str(_id) + "_" + data["id"], "tokens": tokens, "labels": tags, } elif self.config.schema == "seacrowd_text": yield _id, { "id": str(_id) + "_" + data["id"], "text": data["utt"], "label": data["intent"], } else: raise ValueError(f"Invalid config: {self.config.name}") _id += 1