SLURP: A Spoken Language Understanding Resource Package
Emanuele Bastianelli, Andrea Vanzo, Pawel Swietojanski, Verena Rieser
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.- Anthology ID:
- 2020.emnlp-main.588
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7252–7262
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.588
- DOI:
- 10.18653/v1/2020.emnlp-main.588
- Cite (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.
- Cite (Informal):
- SLURP: A Spoken Language Understanding Resource Package (Bastianelli et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.588.pdf
- Code
- pswietojanski/slurp
- Data
- SLURP