Papers › SLURP: A Spoken Language Understanding Resource Package
SLURP: A Spoken Language Understanding Resource Package
Emanuele Bastianelli, Andrea Vanzo, Pawel Swietojanski, Verena Rieser
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Intent Classification | SLURP | Multi-SLURP | Accuracy (%) | 78.33 | #3 of 5 | Archive leaderboard | report |
| Slot Filling | SLURP | Multi-SLURP | F1 | 0.642 | #3 of 5 | Archive leaderboard | report |
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