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 proceedingConference contribution

77 Citations (Scopus)
54 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 languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
PublisherAssociation for Computational Linguistics
Pages7252-7262
Number of pages11
ISBN (Electronic)9781952148606
DOIs
Publication statusPublished - Nov 2020
Event2020 Conference on Empirical Methods in Natural Language Processing - Virtual, Online
Duration: 16 Nov 202020 Nov 2020

Conference

Conference2020 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2020
CityVirtual, Online
Period16/11/2020/11/20

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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