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.
SLURP: A Spoken Language Understanding Resource Package
A new SLU package is introduced with a large, diverse dataset, competitive baselines, and an error analysis metric for entity labeling.
- Year
- 2020
- Venue
- slurp-a-spoken-language-understanding
- Authors
- 4
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2011.13205ARXIV-DEFAULT
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