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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T SLURP: A Spoken Language Understanding Resource Package
%A Bastianelli, Emanuele
%A Vanzo, Andrea
%A Swietojanski, Pawel
%A Rieser, Verena
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F bastianelli-etal-2020-slurp
%X 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.
%R 10.18653/v1/2020.emnlp-main.588
%U https://aclanthology.org/2020.emnlp-main.588
%U https://doi.org/10.18653/v1/2020.emnlp-main.588
%P 7252-7262

Markdown (Informal)

[SLURP: A Spoken Language Understanding Resource Package](https://aclanthology.org/2020.emnlp-main.588) (Bastianelli et al., EMNLP 2020)

• SLURP: A Spoken Language Understanding Resource Package (Bastianelli et al., EMNLP 2020)

ACL

• Emanuele Bastianelli, Andrea Vanzo, Pawel Swietojanski, and Verena Rieser. 2020. SLURP: A Spoken Language Understanding Resource Package. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7252–7262, Online. Association for Computational Linguistics.