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

3 Citations (SciVal)
6 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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