MASSIVE.py

# coding=utf-8

"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""

import json
import datasets

logger = datasets.logging.get_logger(__name__)

_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."
}
"""

_LANGUAGE_PAIRS = ['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', 'is-IS', 'it-IT', 'ja-JP', 'jv-ID', 'ka-GE', 'km-KH', 'kn-IN', 'ko-KR', 'lv-LV', 'ml-IN', 'mn-MN', 'ms-MY', 'my-MM', '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', 'tl-PH', 'tr-TR', 'ur-PK', 'vi-VN', 'zh-CN', 'zh-TW']

_LICENSE = """
Copyright Amazon.com Inc. or its affiliates.

Copyright and license details for the data and modified code can be found in NOTICE.md.

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"""

_DESCRIPTION = """
MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
for the Natural Language Understanding tasks of intent prediction and slot annotation.
Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
"""

_URL = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz"

_SCENARIOS = ['social', 'transport', 'calendar', 'play', 'news', 'datetime', 'recommendation', 'email', 'iot', 'general', 'audio', 'lists', 'qa', 'cooking', 'takeaway', 'music', 'alarm', 'weather']

_INTENTS = ['datetime_query', 'iot_hue_lightchange', 'transport_ticket', 'takeaway_query', 'qa_stock', 'general_greet', 'recommendation_events', 'music_dislikeness', 'iot_wemo_off', 'cooking_recipe', 'qa_currency', 'transport_traffic', 'general_quirky', 'weather_query', 'audio_volume_up', 'email_addcontact', 'takeaway_order', 'email_querycontact', 'iot_hue_lightup', 'recommendation_locations', 'play_audiobook', 'lists_createoradd', 'news_query', 'alarm_query', 'iot_wemo_on', 'general_joke', 'qa_definition', 'social_query', 'music_settings', 'audio_volume_other', 'calendar_remove', 'iot_hue_lightdim', 'calendar_query', 'email_sendemail', 'iot_cleaning', 'audio_volume_down', 'play_radio', 'cooking_query', 'datetime_convert', 'qa_maths', 'iot_hue_lightoff', 'iot_hue_lighton', 'transport_query', 'music_likeness', 'email_query', 'play_music', 'audio_volume_mute', 'social_post', 'alarm_set', 'qa_factoid', 'calendar_set', 'play_game', 'alarm_remove', 'lists_remove', 'transport_taxi', 'recommendation_movies', 'iot_coffee', 'music_query', 'play_podcasts', 'lists_query']

class MASSIVE(datasets.GeneratorBasedBuilder):
    """MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name = name,
            version = datasets.Version("1.0.0"),
            description = f"The MASSIVE corpora for {name}",
        ) for name in _LANGUAGE_PAIRS
    ]

    DEFAULT_CONFIG_NAME = "en-US"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            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"),
                    "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"),
                    }),
                },
            ),
            supervised_keys=None,
            homepage="https://github.com/alexa/massive",
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):

        path = dl_manager.download_and_extract(_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": path,
                    "split": "train",
                    "lang": self.config.name,
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": path,
                    "split": "dev",
                    "lang": self.config.name,
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": path,
                    "split": "test",
                    "lang": self.config.name,
                }
            ),
        ]

    def _generate_examples(self, filepath, split, lang):

        filepath = filepath + "/1.0/data/" + lang + ".jsonl"

        logger.info("⏳ Generating examples from = %s", filepath)
        
        # Read the file
        f = open(filepath,"r")
        lines = f.read().split("\n")
        f.close()

        key_ = 0

        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"],
                    } for j in data["judgments"]
                ]
            else:
                judgments = []

            yield key_, {
                "id": data["id"],
                "locale": data["locale"],
                "partition": data["partition"],
                "scenario": data["scenario"],
                "intent": data["intent"],
                "utt": data["utt"],
                "annot_utt": data["annot_utt"],
                "worker_id": data["worker_id"],
                "slot_method": slot_method,
                "judgments": judgments,
            }

            key_ += 1