MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach
Abstract
We present the 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 51 languages, 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 from 29 genera. We also present modeling results on XLM-R and mT5, including exact match accuracy, intent classification accuracy, and slot-filling F1 score. We have released our dataset, modeling code, and models publicly.
BibTeX
@inproceedings{fitzgerald-etal-2023-massive,
title = "{MASSIVE}: A 1{M}-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages",
author = "FitzGerald, Jack and
Hench, Christopher and
Peris, Charith and
Mackie, Scott and
Rottmann, Kay and
Sanchez, Ana and
Nash, Aaron and
Urbach, Liam and
Kakarala, Vishesh and
Singh, Richa and
Ranganath, Swetha and
Crist, Laurie and
Britan, Misha and
Leeuwis, Wouter and
Tur, Gokhan and
Natarajan, Prem",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.235/",
doi = "10.18653/v1/2023.acl-long.235",
pages = "4277--4302"
}