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.
@article{arxiv.2011.13205,
title = {SLURP: A Spoken Language Understanding Resource Package},
author = {Emanuele Bastianelli and Andrea Vanzo and Pawel Swietojanski and Verena Rieser},
journal= {arXiv preprint arXiv:2011.13205},
year = {2020}
}
Comments
Published at the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP-2020)