MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
This provides a resource for researchers and developers working on multilingual virtual assistants, though it is incremental as it extends an existing English dataset.
The authors tackled the lack of large-scale multilingual datasets for natural language understanding by creating MASSIVE, a 1-million-example dataset with 51 languages, and reported modeling results including exact match accuracy, intent classification accuracy, and slot-filling F1 scores.
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.