SLURP: A spoken language understanding resource package

Emanuele Bastianelli; Andrea Vanzo; Pawel Swietojanski; Verena Rieser

doi:10.18653/v1/2020.emnlp-main.588

SLURP: A spoken language understanding resource package

Emanuele Bastianelli^*, Andrea Vanzo, Pawel Swietojanski, Verena Rieser

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

107 Citations (Scopus)

60 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Publisher	Association for Computational Linguistics
Pages	7252-7262
Number of pages	11
ISBN (Electronic)	9781952148606
DOIs	https://doi.org/10.18653/v1/2020.emnlp-main.588
Publication status	Published - Nov 2020
Event	2020 Conference on Empirical Methods in Natural Language Processing - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020

Conference

Conference	2020 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP 2020
City	Virtual, Online
Period	16/11/20 → 20/11/20

ASJC Scopus subject areas

Information Systems
Computer Science Applications
Computational Theory and Mathematics

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title = "SLURP: A spoken language understanding resource package",

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.",

author = "Emanuele Bastianelli and Andrea Vanzo and Pawel Swietojanski and Verena Rieser",

note = "Funding Information: Thanks to Emotech Ltd and H. Zhuang for agreeing to release this data for research purposes. Special thanks to P. Mediano, M. Zhou and X. Chen for help with designing and organising data collection. This research received funding from the EPSRC project MaDrIgAL (EP/N017536/1), as well as Google Research Grant to support NLU and dialog research at Heriot-Watt University. Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics.; 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",

year = "2020",

month = nov,

doi = "10.18653/v1/2020.emnlp-main.588",

language = "English",

pages = "7252--7262",

booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",

publisher = "Association for Computational Linguistics",

}

Bastianelli, E, Vanzo, A, Swietojanski, P & Rieser, V 2020, SLURP: A spoken language understanding resource package. in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp. 7252-7262, 2020 Conference on Empirical Methods in Natural Language Processing, Virtual, Online, 16/11/20. https://doi.org/10.18653/v1/2020.emnlp-main.588

SLURP: A spoken language understanding resource package. / Bastianelli, Emanuele; Vanzo, Andrea; Swietojanski, Pawel et al.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2020. p. 7252-7262.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T2 - 2020 Conference on Empirical Methods in Natural Language Processing

AU - Bastianelli, Emanuele

AU - Vanzo, Andrea

AU - Swietojanski, Pawel

AU - Rieser, Verena

N1 - Funding Information: Thanks to Emotech Ltd and H. Zhuang for agreeing to release this data for research purposes. Special thanks to P. Mediano, M. Zhou and X. Chen for help with designing and organising data collection. This research received funding from the EPSRC project MaDrIgAL (EP/N017536/1), as well as Google Research Grant to support NLU and dialog research at Heriot-Watt University. Publisher Copyright: © 2020 Association for Computational Linguistics.

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ER -

SLURP: A spoken language understanding resource package

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