{
 "id": "AmazonScience/massive",
 "owner": "AmazonScience",
 "name": "massive",
 "downloads": 12226,
 "likes": 102,
 "last_modified": "2022-11-16T15:44:51.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": "cc-by-4.0",
 "licence_files": [
  "LICENSE"
 ],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "Please cite the following papers when using this dataset.\n\n```latex\n@misc{fitzgerald2022massive,\n      title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},\n      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},\n      year={2022},\n      eprint={2204.08582},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n@inproceedings{bastianelli-etal-2020-slurp,\n    title = \"{SLURP}: A Spoken Language Understanding Resource Package\",\n    author = \"Bastianelli, Emanuele  and\n      Vanzo, Andrea  and\n      Swietojanski, Pawel  and\n      Rieser, Verena\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2020.emnlp-main.588\",\n    doi = \"10.18653/v1/2020.emnlp-main.588\",\n    pages = \"7252--7262\",\n    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",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive."
}