Command Palette
Search for a command to run...
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; Vishesh Kakarala; Richa Singh; Swetha Ranganath; Laurie Crist; Misha Britan; Wouter Leeuwis; Gokhan Tur; Prem Natarajan
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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| intent-classification-on-massive | mT5 Base (text-to-text) | Intent Accuracy: 85.3 |
| intent-classification-on-massive | mT5 Base (encoder-only) | Intent Accuracy: 86.1 |
| intent-classification-on-massive | XLM-R Base | Intent Accuracy: 85.1 |
| slot-filling-on-massive | mT5 Base (text-to-text) | Slot F1 Score: 81.3 |
| slot-filling-on-massive | XLM-R Base | Slot F1 Score: 83.6 |
| slot-filling-on-massive | mT5 Base (encoder-only) | Slot F1 Score: 82.2 |
| zero-shot-slot-filling-on-massive | mT5 Base (encoder-only) | Slot F1 Score: 56.9 |
| zero-shot-slot-filling-on-massive | mT5 Base (text-to-text) | Slot F1 Score: 50.6 |
| zero-shot-slot-filling-on-massive | XLM-R Base | Slot F1 Score: 64.2 |
Build AI with AI
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.