SLURP: A Spoken Language Understanding Resource Package

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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Datasets


Introduced in the Paper:

SLURP

Results from the Paper


Ranked #2 on Slot Filling on SLURP (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Slot Filling SLURP Multi-SLURP F1 0.642 # 2
Intent Classification SLURP Multi-SLURP Accuracy (%) 78.33 # 2

Methods


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